{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46431a62-239c-431f-b4e8-3f613e530234",
   "metadata": {},
   "source": [
    "# Listing 6-1. Python Implementation of Binary thresholding Method based on Histogram of Pixel Intensities and Otsu's Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea2ac327-7c88-45f3-8966-2ec15db1ac36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8d9832ca60>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    " Binary thresholding Method\n",
    "\n",
    "    From the histogram plotted below it's evident that the distribution is bimodal with the lowest probability around\n",
    "    at around pixel value of 150. Hence 150 would be a good threshold for binary segmentation\n",
    "\"\"\"\n",
    "\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "img = cv2.imread(\"/home/santanu/Downloads/coins.jpg\")\n",
    "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "plt.figure(1)\n",
    "plt.imshow(gray,cmap='gray')\n",
    "row,col = np.shape(gray)\n",
    "gray_flat = np.reshape(gray,(row*col,1))[:,0]\n",
    "plt.figure(2)\n",
    "plt.hist(list(gray_flat))\n",
    "gray_const = []\n",
    "for i in range(len(gray_flat)):\n",
    "    if gray_flat[i] < 150 :\n",
    "        gray_const.append(255)\n",
    "    else:\n",
    "        gray_const.append(0)\n",
    "gray_const = np.reshape(np.array(gray_const),(row,col))    \n",
    "plt.figure(3)\n",
    "plt.imshow(gray_const,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12d60112-ca5a-45e1-a8a0-99fd6e57c44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8d975b1490>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "  Otsu's thresholding Method  - Determines the threshold by maximizing the interclass variance \n",
    "\"\"\"\n",
    "\n",
    "img = cv2.imread(\"/home/santanu/Downloads/otsu.jpg\")\n",
    "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "plt.figure(1)\n",
    "plt.imshow(gray,cmap='gray')\n",
    "row,col = np.shape(gray)\n",
    "hist_dist = 256*[0]\n",
    "# Compute the frequency count of each of the pixel in the image\n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        hist_dist[gray[i,j]] += 1\n",
    "# Normalize the frequencies to produce probabilities\n",
    "hist_dist = [c/float(row*col) for c in hist_dist]\n",
    "# Compute the between segment variance \n",
    "def var_c1_c2_func(hist_dist,t):\n",
    "    u1,u2,p1,p2,u = 0,0,0,0,0\n",
    "    for i in range(t+1):\n",
    "        u1 += hist_dist[i]*i\n",
    "        p1 += hist_dist[i]\n",
    "    for i in range(t+1,256): \n",
    "        u2 += hist_dist[i]*i\n",
    "        p2 += hist_dist[i]\n",
    "    for i in range(256):\n",
    "        u += hist_dist[i]*i\n",
    "    var_c1_c2 = p1*(u1 - u)**2 + p2*(u2 - u)**2\n",
    "    return var_c1_c2\n",
    "# Iteratively run through all the pixel intensities from 0 to 255 and chose the one that \n",
    "# maximizes the variance \n",
    "\n",
    "variance_list = []\n",
    "for i in range(256):\n",
    "    var_c1_c2 = var_c1_c2_func(hist_dist,i)\n",
    "    variance_list.append(var_c1_c2)\n",
    "## Fetch the threshold that maximizes the variance\n",
    "t_hat = np.argmax(variance_list)\n",
    "## Compute the segmented image based on the threshold t_hat\n",
    "\n",
    "gray_recons = np.zeros((row,col))\n",
    "\n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        if gray[i,j] <= t_hat :\n",
    "            gray_recons[i,j] = 255\n",
    "        else:\n",
    "            gray_recons[i,j] = 0\n",
    "plt.figure(2)\n",
    "plt.imshow(gray_recons,cmap='gray') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80cf1c2-629b-4dba-b1ae-75c79bee82cb",
   "metadata": {},
   "source": [
    "# Listing 6-2. Image Segmentation using Watershed Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ff89cf0-40a5-410f-990b-e6231e43e05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 24 unique segments found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_28965/1469897724.py:43: FutureWarning: indices argument is deprecated and will be removed in version 0.20. To avoid this warning, please do not use the indices argument. Please see peak_local_max documentation for more details.\n",
      "  localMax = peak_local_max(D, indices=False, min_distance=10,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Oh3HttdeqjJSioiJMmjRJ+YsEXNgXMgVMatX29nZlGvO+HR0dqtRBZ2cnUlNT4XK5kJ6ejuzsbABAQ0MDXn/99bggkfyeyETvzXyX4x5vPsi5IPv3YO4t549u5iayiA6W+hyD8SXkpEv0p1+TiHQ4liSlNkELdngoFFLZEPLP4XAgNTVVtY+pUPTTmBxMfy0tLQ1Op1Nl6OtIWG9QMknm4kUiEbz99tsWKH748OGKqaRJSOFDbc335TPI9EQjKWyILkoTy+12o3///qpNzz33HJqbm+P2d18g3RTuzUTsjSEPhfoUg0mJEu+3eLZ2PKklKZ6Ek/ekppL+ERE8INbe5yRkpodcbMkMezIgtSavtdvt8Hq96jlpaWkYNGhQj+2P1wetra344IMP0NTUBADIzs5GXl6eelfG2KT/xXenNozH1LoEZ7YK/9vtdgwfPhy5ubnqXrfccktcM/FoM5kUOPGeLcddMlgy7TqSTNanGIwUz2lOpIVIyWix6dOno6ysTJ2/ceNGNUjBYBCBQED5VNQ2kumkU01hIM+l+Udzk5OYDBgOh7F27VrljxUXF+Pb3/52wnhMPJDGNE3s3bsXf//739X5o0ePVkytT3ZKcWraeOicBDvku3L1Nq9jLubw4cPVuRs3bsSCBQuSEhBHmhKBK7rVoc8d/Zhubh5J4XBYDGYYxm7DMNYbhrHGMIwVXcdyDcP40DCM7V3/cw66UQkSd6WG07/Lc+JdBwDjx49HUVGROr5nzx4LoEFziRnmHEDGyHRbnYzJ3znBZcIv0TxC+5IBSktLYzSwbLf+Hrz/3r171XFqTPabvB8Ai7nHbBS+E5kmHnRNJuzo6EAoFEIgEIDNZoPT6UR5ebl6zr59+7BmzRqLL/NlMtrBhgd6Yj6J1AJHxmQ8EjD9GaZp1ovv9wD42DTN3xjRvZnvAfDDZG+mx2sS+Vy6I62bjJISIXSRSAQdHR0K7JBggkTTZIxLfwY/U9qTCVjLQ5qgO3fuRGVl976E48aNw+OPPx63fddff70CFNhWmnt33323Ol5eXo7i4mLlf7HdRETJ2AQ+ZF4lJxR9sHh9Jr8zoTkQCKCsrAxVVVUAgAULFuCaa65RAXMKq0O1NhKd29v94v2ua2v9vETX6trsUBntaMTBLgEws+vz8wA+w0EwGCmRHyalrfycSPpL38lms+Huu+/GN7/5TUQiEdTX12Pr1q0YOXKkxacyTVPFvXitNBE50el3SRRSmoyclJzQGzZssLzPn//854Tv//rrr8PpdMLtduOxxx5Dbm6uikvJ95OopzQH6YcxZUtqX/qQ1E4yHMH+k74nNV0oFILT6YTT6cSAAQMUg3344YdoaGhAZmZmXC3aEyU6J5GfGI+J9Hhjb4BRvPbpx46UmXi4DGYCmGdEt4D9sxndVK/INE0WFtwPoOhQbkyzTT1IoIsSHeuJweKhhqNHj1YLHzs7O7Fnzx6UlpYiOzsbLpcLXq/XsjxG1k7kMzs7OxUDBoNBAIDL5bKYqxKVtNvt2LNnD1pbW5N+f9ZZBIATTjgB1157LWbNmoXTTz/dch41pIzX0ceTPqA8PxwOw+/3q3fQhQE/xwNBOjo6epzER0Lq6/c80qRrtZ6YKRGIkiwdLsgxwzTNSQDOBzDHMIzTtMaZiDJhDBmGMdswjBX03bqO9frAeGaj9MckU+pmis1mQ1ZWFr71rW+p47t370ZnZycCgYCS1DKOxHty8SQlPgDFXHrQWeYr0sxkeTfZNmmy6X+SIpEInn32Wdx+++2qjki8dyRD8zPbJGNyXCHAALuslEXNLf1E2cc0faWGSzQuR5o5jjT4cKQ1VSI6LA1mmmZ11/86wzBeBzAVQK1hGCWmadYYhlECoC7BtZYtZLv+Q/9PadOT1Iw3mIkyIhwOBy699FJs374dTz31FMLhMD788EOceuqpME0THo8Hdrsdfr/fEheTmzkYhmExu+SyDwkm0Ldrb29HaWkpZs2ahdWrV8M0TQwcOBD9+vVT19HE4z2XLVsGv98P0zRRVxftwvr6etxyyy3K/EtJScHgwYOVaSj/y2U2ZAYKB7Y5LS1NVSaWaWKyv+RYyFUGuhDQhV1vme/J0NFiAjmnDuZ8tuVg2nPIDGYYhhuAzTTNtq7PswDcD+AtANcC+E3X/zcP5f4yfiPjODqzxfPHemizilXNnTsXgUAAzz77LFpaWrBo0SKcdlpUAbMeop7iJLULJbrMVKfmY0DX7/crbREKhZCdnY2zzjrLEqTmxJZFbyKRCGbMmKGesXPnTuzYsQONjY1KawLRBOWKigrk5eUp9JIMxXbpWp2arbOzEw6HA+np6So2lyhWJDNTEk1OqbWOtlY4EhQP/DgadDgarAjA612NdAB40TTN9w3DWA7gFcMwbkB09/UrDuamHEg90BsPuNDNF10DJoK+7XY70tLScNFFF+H1119Hc3MzGhsb8cknn+Css86CaZpwuVxwu91q4koTkUFpMo6EeDnBW1tb0d7eDrfbrdKoqPUIRNhsNpW+xBr4fA/WuE9LS8OIESMwcOBAHDhwAIsWLbIwmdTy0kyWK7RlP8rrqLlcLhf8fj+cTifS0tIQCARUP+kZIBQEOupIYv/Y7XaVNrZz505lEbCtbrcb5eXlalx03y8eKtwToqgDHTw/Gf/qYBjtYM3fQ2Yw0zR3AjghzvEGAGcd6n1JUoNxAOREigdgaO2IMRF5jJNl6tSpePTRR/G9730PbW1taG1txfz58zFw4ECMHj1alWUjBM8MejnRaAry+Z2dnThw4AB8Ph+Ki4vVhElPT1dahf4PA9tc9cyJrZPL5VKTf/r06fjiiy9UMR1uGi81LH1GHdGLp/2DwaBK8+I9WVuEQkCapEDU99y3b59q3ymnnILs7GwldGw2G9566y1s27YNpmni8ccfR0tLi+WdhgwZgiuvvBI2mw0nn3wypk+fbokffhm1+5NhwHia+mCozy5XISUDnyZCE/XzdWlvs9lwzjnnKCbz+XxoaGhAQ0MDdu3ahWHDhqG0tBTp6elqmYrcGpb3JnO0tLTA7/fDMAwUFhYqPwfo1ga8BxNqac5Jf47ChMcIvoTDYfTr1w+zZs1CRUUFBg0aBI/Ho85jTIsahH5YT1KXoAWFjozfETUEoIQA20qIHgDOOuss5OTkIBAIoKamBt/5znewfft2lcoVjyoqKvDLX/4SAFBSUoKxY8fiiSeeQFZWVkwsNJ5Q/bJMvMOlPs9gOknTQUcM4zFXIsAE6M6mP++88/DEE0/g9ddfx1tvvQUgWt5t6dKlcLlcGD58uNr4QWZneL1eFaQ1TRP79u3DtGnTMGzYMLS1tSnkUOYi0q+jLygZjwwmF20S2aMmtdvtKCwsREZGBlwuF1pbW9X7yNQtli2g+cx3l+lf7BdZCUueB8CCQlI76uahzWaD1+vF3Llz8eabbx60lK+pqUFNTQ1GjhyJm266Ceeddx5mzpypfj9WmCke9cmybUDsnszxYHn9mPzfdV/1X2pCIoOcMJxcfr8fv/zlL/H//X//n8XP0YlmEKW7JI/Hg+nTp6Nfv35qrRV9kc7OTrS3t6t76LXudV9KSnIyIM06aj/2kZz8UtvQz5OFd3RAgqAHQRLei4KE7aV/uWzZMmzduhUAMHToUJxzzjlYv349Fi5caOl79vnEiRMt7bfb7WhtbcW2bdtixhYACgsL8fjjj+Occ86J8avijS1JB2mSYcxEqGA8wSxp//79SZVt67MMJhkm3tKVRN+1+1r+S2Kn6TGfYDAIr9eLH/zgB1i/fj2qq6sP+n1OOeUUjBkzRuXucWK1t7crbULNQP8H6DbDGADmpGQKlkQEfT4fgsEgXC4X2tvbFYDC9V0ejwder1fdh1knegKz7GNOJvYJGYxMYbPZ4PP58MknnyifSq4o4D0LCgowdOhQ5OfnK3hfxhbZ736/H5FIBDU1NaisrERDQ4PqQ6fTiSeeeAKTJk3CkCFDYsz7nhgs0ZjHI53BpCUkfdZDZbA+ZyLqzBJPk8n/QGIp09N3XqPv08W1W88//zyWLFmCTz/9FP/7v/+bdPtzc3NRUFAAm82mtCAZBeguzU0/iX4OfSzGqgjvE6lkH1C7sCwBUcj29nZ4PB6VJtXW1mbRQCyzJut+SFCCfcPkX8bg2FesSbJr1y4LYCGZvqioCP369UP//v0V+MM2ENkkw1OQtLW1IS0tDWVlZaisrMTu3bvR2tqKYDCIG2+8EZMmTcLzzz8vJ7OFegIiktFeic7Xn9Xb94TP6GsarKCgwFL7vSftFe+/dt+43xPB+Pqz6J/U1dVZnrN69Wo0NzfjjDPOQE1NDW6++WaVxDt69GjMmDFDTVJC8AxQEyRhHqCsf8E2SCbkNYTK5bsSXElJScHOnTuRm5uLzMxM+P1+tcMLtSMZNzU1VeVdMgSh+1yGYcDlcqmsDjLzgQMH8MEHH1jq9wPRUAJ3kXG73ep8vo8UgPL9KEDI/MFgEC0tLairq7PUjhw5ciRef/11FBYWWuow6uNJQaH72oko3vyQoFC8e/B7TU0NBgwYcGxpMAmhA93xkYNdkpAMxZNIunkARCV6aWmp5Rp+j0QieO655xRzORwOTJkyxYLmEaTg5KZGIlMx272jowNOpzOuaSxBCqfTqTSRzIRnwNhut8PtdiugJBAIqBgbNQgrEFPLUNNy6QtBEgCKWXw+H3bs2BHDXC6XC5MnT0ZZWZmlrgnXwJmmqZ4vywvQ76MGTk1NVVWsMjMz4XQ6sWzZMoRCIWzZsgW//e1v8bvf/c6yyvzLosMBWfrkgksg8TKDI9GxOkii2/Tyj8f0/zIuJ0kGTEly4ukJzDS/aFI6HA64XC6FGtKk4oSV664AqCz3/Px8pKenIxgMor29HWlpacjMzFSl4wiydHR0ID09HRkZGcq847ZM1KpOpxMpKSnIzMxEamoqfD4fbDZbjD9qGAamTZuG8vJyuFwuVdeDeZ18X9M0LVkrBGOIvlITs6YJhRo3nQeiqwvee++9uADXl00HMwf7lAbT1XI8P+tIkGEY+OyzzxJuwmCz2XDllVeqCROvnRIM0O9N34pag/fUmUNC3jSXqKWIbvJ+Ho9HmXlkFjIOJ6/X60VDQwPy8/PR3NyMtLQ0eDwetaEgNRbDC9xrmiAJN50IhUJqI8HW1lZUVVVh//79aGtrs7xrfn4+ioqKkJaWhrS0NPj9ftUnDDPoCc5AFDzhZ76/tFSYyzl06FCkp6djyZIlaGhowNVXX40XXngBs2bN+kqg+0MR8H2KwQ6VdNhZMqgECTo6OvCLX/wCO3bswOrVq1FbWxv3fjabDW+//TbsdjvKy8vx85//3KJJgChz7N69G2+88Ya6buzYsRZYvKOjwzKZyJSceBQalOSScWXlYPkuPp9PBadl9n1tbS0+++wz+P1+FBQUYPTo0QgEAson4iYXLDknfTu2kcBLZ2enSvWqqKhQteol5efn46STTlJFfWRFLfY5mYB9IoPqJImQ8p0BID09XY2l2+1GR0cHgsEgnn32WZxxxhmWSsQSEDucJGNdoMtj8X5Lhvokg/Wm/uOhP4k6IhwOY8eOHVi2bBnuvvtule7UE0UiEbV3s2EY+Pvf/44f//jHOPXUUzFy5EjFaF6v16IF8/PzlclGswjoTuOixtGFAf1OTkbphwLdYIdMf6KPx+e9//77ChTYt28f9u/fDyBaQrusrAwFBQWWwqkAVJyOKVvUQO3t7WhqasKKFSvirgsDoCr+0rQlAso2yloeAFQwm+2XgBLRRplUTDAoJSUF06ZNw/vvvw8AmDdvHl5++WW15Eg33+Xi0yNNh2JB9UkGOxjSIXrJnJFIBHv27MF1112HTZs2HdL9TdOE3+/Hj370IwwdOhR//vOfMXr06JiMCKB7UtAMjOen0RTSsyMo2SWDybVdANQqZFnfkM/QhQa/79ixA1VVVTjppJOUJpB5nhKiT01NRUVFBWpqaiwxKZ1Y+CYtLU2BJhIlpRaTsS8dvGLf0YymJuc7SWGTkZGB/v37Y+/evYhEInjppZfwta99TZUo0Pu3L9Exz2CJNFdTUxPuuusufP7555Y9stxuN1JSUjBw4EAVCNUHpaOjA6tXr1bMQt9jx44d+Na3voUXXngB5eXlCQuZ8n4EFKR/ISUuYXJOMoIYMhjLd+P1lPYyi6S9vb1H6RoIBLBu3TpcdNFFSE9Ph9vths/nQ1tbm6pr6PF4sGHDBlVpizRgwAA8++yz+Otf/6oqWdF0ZuY9mZbaWI4DAMs76YxNxpQLP6XwAIDMzEzk5+erYj+LFy+2ZNp8Ff5YsnTMMZhkqHhIIDXO3LlzVV4hEJXOw4YNQ0lJCbKzs5V01AvE8O+MM85QUnXXrl2oqKhAIBBAbW0tzj33XMyaNQvf/va3Y9pH042mIIESmcYjpT1NJDIhgQj5vgQ2AChzjOhjKBTCqlWreixhnZmZiVNOOQWdnZ1obGxEU1OTYljW+ujs7ERDQ0MMo955551IT0/Hxo0b1bGBAwdaYnKM4cnUMwon/T2kX6YjjdTGZFgiqpFIBEVFRXC73Wpz+VdffRW33HJLjJmo+2FfNeMdcwwGxAadSZFIBF6vF3fccYdiLpvNhpEjR6Jfv37IzMxUgU3GewgRc6JziYrNZkMgEEAkEsHw4cPRv39/VFZWYtOmTYhEIpg3bx58Ph8uuugivPPOOwCAFStWoLS0VK2xooaSzr8O9cvcPyA2PYnnSilPkmBCPKKPNXXqVJWGJZ/LUglMr5o6dSoWLFhg0fgPPfQQ7rrrLqxZs0YdGzBggNIw0qcDoJKZGdtjZggRU1kDhO9JkIPjEQ6HlUandi8sLITH41G5nK+99hpuvPFGi0+3f/9+PP744/j888/V2CeiJ554AsOHD7eYmfF8+sNFr/scg+n+lIS0dYmom4dNTU2YO3cu3n33XXXe6NGjFTDBDo9EIsrsY8ItA5iRSARNTU1KegJQG9iNGDECdrsd69atg2maluRWAGrTB6JhjB8B3QzFz2QYoLuwjkzbIiPpvgv7gYm3MumXlJWVheLiYowfP17F4OS7y2fSR/L7/bDb7Tj55JOxZMkS1NdHK/FVVlbiu9/9ruX+hmEoNJPvIbMyZKEguXqA/SLfh6EH9j3TwLgmTDJevLioaZpYt24d5s+fj/vuuy/hQlCdTj31VJx99tm4/PLLcfXVV1vAFaatHYm4a59jsN4oXmyMg3zPPfco5jKM6Baxo0aNskgyTigAKt7EbYc4oFxmwgpKcmO+gQMHwuPxYPfu3XETgeVyEbnUA+jO8+M7yDqGslqTHGwyFScpSxlwEoTD0c3yDhw4gM7OTmRkZODkk09Gfn6+JbjNd6bJ6vP5LEVvKETS0tJw6qmnorq6WpmeugRnm7jezDAMFZLg8wBr0R/JRLyHDNhTY7MP2H8MG3BMSXyHn/zkJ1i8eLHaBONg6KOPPsLnn3+O1157Dd///vdx1llnqXkgNePhMNkxx2CANa2Jf9u2bcPixYsBRAdi/PjxGDlypLqG58tVszRZOFnp68gcP/o6RPrS09PRr18/FBcX491337WsQqb2Y00PSmhC4UA0vkOiGSpBAmY7SHjbNKNZ/lziz8wLbo2Um5uLWbNmYf/+/Rg0aJDFj9PzK30+n/IN7Xa7CjL7fD61o0p+fj4yMzNRXl6OLVu2xMTBWHKBpi1zFmVogtWAZYqYrp0pSMiEDKHIUAT9Un2St7W14Qc/+AHeeOMNS9uysrJUlS/9GnlefX29Elzz5s3DggUL8Oqrr+Kss7oX43PMDydV75hkMMBqu1dUVODmm29WsZ8xY8Zg6NChlvNpIlJDdHZ2WjImJJNJs5HBWgmr0zwaOXKkxTeJRCLYtGkT8vPzVTYEGYv3ZxoTTSkyFM1RCaFTszF3UE+iJeBB7TpgwAD1WZqWRBzT0tKUL2SaJurr65WWI2PIFcxutxtjxowBAAVyFBcXIyMjA0B3AjHQnYmi+2XMNyRTERCRJrOE7Ak+8b1kSTnJIBUVFWp3GY7vmDFjkJuba/GrpFaUAf3KykocOHBAWSF+vx833ngjnn76aZx55pkq1nk4/hdwDDKY9Ls4WRYvXowtW7YAiJp9JSUllqUaepxILg/hZASik4qTgeYSJwsnk81mUxujl5SUYOvWrapADQCViwdEdz1hCpHMkvD7/WpCUpNwEsj0KaA7TsSCO2QI5g0SNaXPKOFtChMyFtvHe8qEYRm/k4CMzWbD8OHDYRgGvF4vpk+frvqLJirQvQ8aj8steWkqOxwO1TfcxwyAxRSTWo3b9Eqtp1NmZiZKSkrQv39/ZGdnK20oLQAAylLhs0pKSlBQUICRI0di1apV8Hq9qKmpwZVXXokXX3xRLfY8XJCjT0XlpDkjKdF3dvwvfvEL9Vt5ebnaXoeTjZ0LdPs99GmoBYHu5fHyfN1EoDnkcDiQnZ2tth8i1dTUYPHixejo6EB7e3tMdjslKE0rtpOInt/vRyAQgM/ng8/nU2YszU7GnlpbW1VSrcwQiQeqkGkkaik1g6xbL1FMvmd6ejrGjRuHs88+GwBU38mxkIzNrA2ajSwaJC0ICq1AIIBgMKiEhyysQxMtUTZJQUEBTj31VIwePRpFRUWW8ufSn5WlE8io2dnZyMnJQUZGBmbOnIkTTjgBNpsNbW1tuO666/Dhhx9aQCX26cFSn2IwPcYlJ7b0t6S0feihh9QCwPT0dFUNihqDA9ne3g6fzxfzLBmTkVokGAxalldIKFnGpYYPH67y/Ej79+9XuYher1dpPRkTkxnyJLaTWlS+K81WhiKYR0ktxoWinATBYFAxKUMFcmsmAKqfZEyQjECYnf4WALUIk+1iuIP5iDJzRSJwcuyysrKQkZGh4oA8j20EoLQqmZ99PXHiRAXGzJgxA1OmTFFCR6ZiMfmZfUuklfeR6+CYqDxo0CBV2qC+vh7XXXcdPv30U8tKbX0uJkN9isESUbyXoUSqqamxpBRxT2FKQ3YMM8ulryWRLQkJy+dx4PQgKVEvOaklffTRR2hra0NLSwuam5sRCoWQkZEBj8cDAJbJSw3m8/nUWig+k/mGRDUbGxvV9rWGYSjtwDaTUaTPKKFzILZKL30VMhu1tM1mQ0tLi4oNyuKosvoU+5SAjewnmo48RgaXWSoyRYqCzePxICMjQ/WvYRjIzMzEhRdeiFNOOQUFBQXIzs6O2diCWpE+tgShJAPK/dLYb+Xl5Zg4cSIcDgfq6+txxRVX4PPPP48RGAdDfYrBOEmk2aZrNfmSTC4lyeyCzs5OeL1ey/ZEfr8fXq83JvqvP4cMJ6styXNl6k+iTvd6vZg/f74y5xoaGpTvR5RPFht1OBxKsqelpSE/Px95eXlIT09HJBItv93Y2Aiv1wuXy4WcnBzLtrRcw0XNyXbLvtTTmXgtGYUM5nQ6VW0SoFsYUGBJvwvoBjLoJ0rTTGb9U+hRo1BoyUA1Jz33I5PMCkR9qYKCAuXDSSCDNT6YoW8YhtJ4HKdAIBATU2Pb3G43Bg0ahGnTpgGIIpU33HCDQqcPhfoUgwHJZSyTCebNm6eyKADg5JNPVva2TDuSji4ntb78HrAutuQ5cnLIwCgHkAMej9ra2vDZZ5+hvr5emXX79+9HJBJRSCJNRi5YlJOpo6MDzc3NqK2txYEDB7Bnzx74fD4MGjRILTmhaUjJzQlJc48TURda8UIdUhOwsjHNWmmqkeG5spqCTmp7SbK/qeGkBuG4EASSTELtK0uYS2bnuLFsAc1ZuchVai2Px6OyWmQQG+hGQj0ejzL7a2pq8O677yp3gc9Llvocg/VGujmgD6Zcb6TvHCL/gO5l7RxUmj6cMDRXpHnIP04Yu92OsWPHWtrwjW98Q0HZfr8fX3zxBVavXq2W+zc1NaGhoQGtra0q4batrQ0dHR3w+/1oaWnBgQMHUF9fj5aWFrWL5MqVK/H5559jwYIFMelFbCcAy7vwD0CMWcT+1PuVk559IUEICpympiZV90MyH2CNOUqEkgwog+Q6OALA8hxdIxP55We2lZA+n893lkCPbupxDnGekHlzc3Mtm70/9thjvSZUJ6JeYXrDMJ4BcBGAOtM0x3YdywXwMoCBAHYDuMI0zSYj2vLfA7gAgA/At03TXHUwDdLhWJ0xaMPHe1kOTEpKijJv5MRgQBfoXlVLySTz9DgAenoRAOUPyXvqEm3q1Km4+OKLce2116pj1dXVWLZsGU4++WR4vV61tF+aXVJKypXEEv0CgJUrV6KjowPjxo1TjC8zHuQiU/5GP48aQWbsSzNZLptxu90KaODvQDRm19LSoipXuVwuFfcDYPF7ZOCZJrKs0yg1kWQSMlEoFEJTU5PyS+UaO6LA9P90VFiaxDLQLbNHZKYNV3kDwLBhw1BVVYXm5mYEg0HceuutajXBwYAcycTBngPwBwAviGOJtok9H8Cwrr9pAP7Y9T8piudn6dST30PAgk45YN2kgAiZLjFpRkmIm0TJRsaWELA0YSS99957yMvLi2lfY2Mj2tvbkZOTo45RijJ47XQ6UV1djRUrViTMq6NAoOnFQDQnhzQz9WvIZDRN2Q+y3xkj09+X2ojJtx0dHWhpaYFhRKtQEXmUTEJ/WKKiOklwieND4dXU1ASv12vJ5pAxLvqvUjDy3SQzcQw5pjSjJXrJ/9I/JsnNEw9Gk/XKYKZpzjcMY6B2+BLE3yb2EgAvmNEWLDEMI9vo2iss6Rb1QnLy64NFiUyTRgIRRNw44FKKEcKWGkRC97y3XHnMFKiuPrK047PPPrO0yeVyqaz2cDisEmnZfoIB6enpaGlpwfr162OYKzc3F01NTRY/gIzN1CBOfjK+JGk+yUAzTSdqOXke7yG1A9tdWFgIr9cLr9eLQCBgQW2ZYSKD5tLn472ZQkXGZ19wHJkKxq1ppV9MzUrNLfMH2UaeJ4l9w/vIeJnH47GY25MmTVIr27/44gv84x//wDe/+c2YedgTHWomR6JtYssAVIrzqrqOxTCYYRizAczWjvX6YOmMxyP6GNJMYEdLgELGmDgYModNBijZNmoB+exIJJKweE5+fj7y8/MxatSoGJNEmjfS9HW5XDjhhBOwfPlyC0J66qmn4r333rPEzaQWkqghQQ22W8a+pFTmcb5vJBJRk1UyoNRIcnnNvHnzUF5eripUZWZmqtXJNGul7yNRQ117krFstmh+JkvQFRQUqEwVCg8ZgJbZLfL9JKNJ4cjvsvybzI4hOGSapvKjASgUV/Z5MnTYqVKmaZpG1w6VB3ldzA6XXZ+Tup5ZB9LfkOXCiDRJ5ExKSf2/zkzSxuc5nIwSYDEMIyaT22azITMzE5MmTYLb7QaAmCwDakuZNMtJmZ+fj2nTpmH//v2oqKiAaZp48803Lc/Yu3cvRowYgdzcXAXGyCwMCczozCaFjJzoOvghg7dAN0P6/X68//77qK2tRXV1NTweD3JycjB58mQ4nU5kZmbCMAyVR8l+ktA9GZXWA3Mym5ub1b5q+fn5sNmiBX3Yb1zKws0tZNyOpIMu7A8JBJGpyahAt3/N9uokwbJk6VAZLNE2sdUAysV5/bqOJUXSJOvpJXje2WefjQsuuEAtrly8eDEuvfRSdZ7MzpYFX7g8RQZBgdhtZ+VElHsT8540yYqLi9XSexbiLCkpUWaknJj0GXh/6exL8KS0tBS5ubmw2Ww4cOBAzFZAWVlZarWv3W5XicsSfUvUr5TanOAyMZiMwERjicIS/Zs/f76lIhdNxVAohNLSUpSVlcHlciE9PV3F8dhfnNh8fzJxS0uL2jpqzZo1akyGDRuGcePGWfbB5jIZvg+TpiXzEGiRS10I5xMooSbjGDDeJ2N2ubm5agHq2rVr4fP51JZRydChMliibWLfAvAdwzBeQhTcaDkY/0uidxyMns7VnXl98kunGYBlsuvQu/QNZJZBvEkhzdRIJIIJEyaoSc0kUgZsOeh8rg5nS79C+iyMDY0ZMwbt7e1YuXKlpRBNZWUlWltbUVpaqoALCblLk4hmI/tDmm8SMJE+CcuwAbCkQe3fv18JE44V+6Ompga1tbXYt28fysrKkJeXpywNl8ulIHXemwnLNpsNGzduRH19veXeALB161YEg0GcfvrpFrNdal89v5QmXigUUsuGmBLFeKE0taUA1o8NHjxYMdgLL7yAe++912I69kbJwPT/QBTQyDcMowrAfYgyVrxtYv+NKES/A1GY/rqkW5IESe1CZhg0aJBiCjrGNFFk7TygOwdNLnyUTj3PkROVJM1CuYaM8bITTjhBrSGTmjBe1oRkemm+MWAs/SBqXe5sSU1G7cV7sLConHg60MH3kcJCmpU8JhlUAhAA0NLSYtli9qOPPsIHH3yAt99+Gxs3bkQkEt0tpa6uDoZhoKSkBDk5OQlNrm3btlkyQOIRN4EHuje7kMF4HgO6F9RSYDIgzw0SqfGl1SCFptzNs7Oz07Ic6ec//zlKSkqOOIr4zQQ/xWwT24Uezkn66XFIaiTdrNEnvs1mw5w5c/Dss8+iubkZfr8fmzdvxpQpU5T/xY6Tf4xd0RSQ2R+U+tIHs9vtKgOBPh8ldzgcVpKRZhfBAd5H7tcFWH0+qQ0pdaVvIDXc5MmTsXr1anR0dODCCy9Uz6em5HOAKKPE27+MzydR+0ktKjUc0B0z9Pl8FkDna1/7GsrKynDbbbfhiiuuwB133IFPP/0UQDfoUllZqWr3J0MOhwNz5szBxx9/jA0bNljmgvS3pRZjkVIdfAqFQjF5m1L4SSHIPpHjz/EgcVWEvLbX90n6zb9CkszF7yS73a5KRQNRtIcbKegThpNGDy7ymEyt4TPkGiYyjsxyIGORYSVaBkBlIkhmjqeJ6edxEhHNojQmaHPqqadi0KBBKCoqwtatWxWMzbQwah22VYI7bKNE8sjIElGV2RcyPsi0LSC6cuGSSy6B2+1GS0sLCgoK8OSTT2Lv3r249dZbsX//fsvqhd6opKQEZ599Nq655hr069cPVVVVFgYLh7urHbPfJUnwgX3KsWcMjUCOHF+alxSuet/Ee8aXiiIeSepNKrBz1q5di4ULF6pJMG3aNIXkVVZWYujQoWrxobxW97ckcWJJmFeCADKVhp2/f/9+ZGdnW3Yw0TWANCupCaS/JyWvND15Ht+RuXX0Iex2O/Ly8lBdXa20hdSiRM0ouXlchhoYQ5KTSiY56xkcrNYEREu37dy5E0899RQ++OADnHTSSSqAfu211+Kf//wnpk+fjsWLF1sYRaerrroKHo8H119/PYqKimCapgXE0McIQMwYsd0UVDpwxHdgf8vxZzyO4QF5T52kJXW0QY6jRrITaWYFAgGsX78e9913H4BoaehEsScAWLduHQoKCtTuJJ2dncoWB7pjL9LfoBnIZ7MtMmsb6Ia6W1pa0NnZqRiC18l2A92xIx2B1DWdjBUxlsSALYkM0tzcDJ/Ph6ysLFRXVyMYDMbUJpRxPcLTUntLc0qCLTJoKwPvmzdvVulnALBp0yZLteQlS5ZYxsAwopnsDQ0N+N3vfochQ4ZY/F3258SJEy3xLY7H9ddfjw8//FD5fBs3bsSkSZNiwiZkKN3UlaakfKa0hvg8Vv8ik3J8Vq3qzvKbPHkyvva1ryVtGpL6FINJM41S9M0338Rf/vIXLF68OCZPMRE1NzejqqoKJSUlKkOCSa96rIOZH1KbyBoSZlfA0W63q4IxXHHM5SWcoLqzraOi0reTPp8EXPT3l+2Q+ZN+vx+5ubkoLCxEVVUVnE6nKg0gGRhAjBkotVgoFIrZpwyIggeZmZmIRKILPKWmTIZM08TSpUsBAL/97W/xox/9CGeeeSaysrJUfyVCig3DwKBBgxRIxTawL/WEX/YNf+c1elk8joFkMgn4yOc5HA7LTp45OTlqX7iDoT7FYHIF8Ycffojnn38eb7/9tkXaknqSJCeeeCImT56M2tpatLa2qqI1nEhSw3AyckkE04JkLppcF8brWZbANE3l78lgrWQcPXAp/QcJ29O8pHMtQw6yD5i7mJ2djW3btqGwsBC1tbUoLi62TDQyEIEdmpC8PxFLpjoRsSQCy2fKtLB4fd+/f3/U1tbGbBzPSVxVVYVbb70Vp59+Oi644AJcc801FkbQ76f7SUD3bp4SPeX19IvY17rvpN+fwpBpWvSNOzo6lJCqqKhQfr3D4cAtt9xiGddjEuTg4M+bNw///d//bSkmY7fblY1fXFyM8vLyGCnGyeF2u9HU1AS73Y62tjYVD+H+VyzEIuFnTr6cnBzL0nIyDjevY7JudnY2GhoaVEfTDIsHyMg/GWvicyVsLM05Xk/fi+2JRCKoq6vDq6++itraWpx22mkoLi5GQ0ODqjMha4fInEyZWUHTl9qLY8DFm6FQCM3NzcjNzcWFF16Id999F16vFyNGjEBGRobF/Bo9ejQAa9hhxYoViEQiaG5uRiQSweeff45FixbB5/Ph5ptvtsS1ZH9Ryw8bNgwHDhwAEM3SYUk8hjMogCQsz3HjnOHYANYtZmm6S7RU7o9WWVmp+mTkyJGYMWOG5fpkfbA+tUcz9+26+eabLUHVsrIylJeXKzueTCJhfInWScCCjMN0ouzsbEtRG15PycdJzkFhuo/NZsPmzZtRVVWFlJQUnH322ZZCm3ooQJpqPEcmC8czG/UJIkmaRV6vF4sWLVJ9ZBgGTjjhBGRmZqql9Pn5+TBNU60zk2EAiXBK34VgCJHTxsZGpKenw+VyKU0nl8HQ3JZAggxhsA+3bNmiqn4B0Yn/i1/8At/+9rct/SDjeuFwGKtXr8Y3vvENdd3pp5+O0tJSZbIT7KEAlKa2jDvGs4BYagGwbq2UkpKCxsZGvPbaa+rcv/3tb7j00kstvjhrUHb15bGxR/OHH36Ie+65B83NzUhJSYHb7ca0adPgcrkU2CCj9FwizsRUnseaDPRXIpEI8vLyUFtbi927d6sUHi7U0210+iytra1obm7Ghg0bYBiGqosOAO+88w5mzZpl2X5Vmj06UknNwAwGyXi6acnJIaW59E+5UQPJNE2sWbMGTqcTp556Ktra2tDe3o6srCzVb5T4fDf6n1lZWcrncDgc8Hq9qjSBLIDDycxwACejjjgSHbXb7apIztixY9GvXz9s2LABLS0t8Pv9uP/++9HR0YHrr78+xhykEBo+fDhuvfVWPP300+js7MTKlSuVOc4Mez5PmvE8xs/Mc+SyHgBq108ZlHe73ejs7MT8+fNVWy688ELMnDlT9TPPTdZE7DMaTH5PSYluulZcXAzTNC1L6SmByVQ6QsbJItOimOXgdDrR1NSEjo4OVFdXw+v1qoklnW5e19DQEJO6I2ny5MkYPny4ageZTIeROSHpY8pqTnr8hgFiCgdqFcZ17HY7qqurMW/evITtKiwsRGFhIYYOHYrU1FRkZGSoisJ8P6DbJGc2RGtrq6pElZGRgezsbEt5bDI52yhjQtLvkWglmYAZL+3t7Vi8eLECLX72s5/hpptuUteR2M5gMIhLLrkEW7duBRDdgH7atGlqWX9HR4dqi8zokbFFgiD8zLgYAR273a5WBGzduhVffPEFOjs74XK58OSTT+LrX/+6pX6lYRioqanBwIEDjy0NRpo4caJCbGRak4x5UGJRU3ByUu1zUzguvqQJmZ+fj5qaGmzbti1hpkM8MgwDWVlZKqkVAHbt2qXKc8fbMIDtpqnEtsqJpGcWyEKnUivQKQdiU6Buv/12vPLKK2r1bV1dnapaO3jwYHg8HpW8ymx0ts3v96O9vR3BYBD19fUoKytDYWGh2oOMFgLPJ8NJUEEGr3Vggu9BZrbb7ZgxYwY+/PBDhMNhPPjgg/B4PPjmN78ZYyaToX/4wx9izpw58Pv9aGhoUH6Zw+FQ+6/RX6L2kuAVfU4uCpULVB2OaLGhQCCAbdu2YcmSJSqs88gjj+D//b//F2MGH9NxsKysLBQUFMSgQTqSxoks04r4O5M+JYTLzIu2tjbMmzcvrl0ejzIzM3HmmWciIyMDd999N+bMmaNMCD6fk09f2QzELh2hacY/3dTg+cxcoI8kMw50Gj16NBYuXIg333wTP/nJT5Rp1tTUhJUrV6rznE6nsgr43MrKSss9Kysrcc4556hnynVhcmJx0nGMaDLyHWn2Mo7J+om0OAoKCrB//374/X589NFHOPfcc5GdnW25NxAVXKeddhr+8Ic/4J577sGBAwfwxRdfYOrUqWhubkZxcTGys7NVwJ/MIy0Bfmawn5W92Oa2tjbs3r0bCxYsQDgcRmZmJn7729/iG9/4hhImHG+OUbLUpxjM4/HgxBNPVGuo2Dl0wGWUXq+xoKNRcvk3AIWM+f1+DBs2TJkcJD0xGACuuOIKVYePk15G+Pl8Mg0ZTQ6KhKFlzEX6WPI+PM4Jqsev4hEl+VVXXYWioiK89tpreOedd2LSiYLBIPbs2dPjGPj9fmzduhXjx4+PCYhTA5EICsn26zmN1Mo0yVwuF0wzmn2zbNky1NTU4P3334fD4cCTTz6p+kpqs9TUVJx11ll4+OGHcfPNNyMYDGLp0qXweDyYPn06AoEAPB4PXC6XqkvP4DtNd1kqLy0tTYFXlZWVWLNmDQ4cOKBMywcffBCXXXYZbDabqm5FOhiIHuhjDHbqqacqGJa7fgBQcRpZbUhP+9EnJhEu6VNxsMePH4+2tjbs27cPADB06FC8/fbbqh08l6XUyCSRSATDhg3D/PnzlVRsaWlBTk5OzNJ4PdWGk5ECQ05U6YPJ8zkxCK0ToZMB0MLCQrU8xjAMzJo1C2effTZ+8pOfYO7cuVi/fr1lQ73eKD09HePHj4+bM8n/MveSRCHH8ZBJ1DLuFg6HVb+WlpaitrYWkUgECxcuxI4dOzBs2DCLyc97GIaBM844A0899RTuuusu1NXVwev14pNPPkFpaSn69++P3NxcVXbN5XKp0nNAVAixYldTUxNqa2uxY8cO7Nu3T/mk2dnZeOihh2LMQs6jQ6E+xWBMTeJeU3IwpYmlq2op3eXEkGal9Bf04KbNFq2gJNEnEjUk7zN79my88MILqjDn3r17kZeXZwFK2F4yHU0RtoHomzQxJUQtmVRqBl67du1a1b4zzjgDU6dOtbyP0+nE4MGD8frrr+Pdd99FVVUVDMPAnj178Mc//lGdl5qaiosvvhivvvqqOjZ8+HAFAujamu8iTWFZnkC+L81LajL6gAzm8lmbN2+Gz+dDc3Mz/vrXv+I3v/mNeh4FotTiM2fOxG9/+1vcfPPNqix4ZWUlqqqq0K9fP2XilZeXq34jihoKRbcD7uzsRFVVlSUcYrfb8dBDD+Hyyy9XAjCegJF9kQz1GQabMGGCKvslO4XFNCnBuTRBwrDS8ZSTQjIaYN1lZcyYMaokGpNWb775ZkubpNQm4+gMyIGXVXYBxPhX0sySKTvSDJP/+VkuwHQ6nWpNE6m1tRXbtm3D+vXr8fDDD6vjubm5eOqppzB8+HAMHjwYxcXFcLvduPnmm5UmSUlJwfLlyy0MVlRUZMnG5/tTAxNxlO+pT0gJRPEd6UvK+vwAMG3aNLXE5Y033sAFF1yAmTNnKg3i9/uV1q6trYVhGPj3v/9tKQ3HseWymJqaGuzatcsyhjxHzzax2+0YOnQo5s6di8svv9xSoYp/UpDr9+yN+gSDud1uDB06NMbkoNkhtZaUOnpwV/oJ7Bi5gFLeV5YBY+096UvopLeHJDMmdH9Fb7MsriKzUABrcRZOBC73YKwtGAxi8+bNlgyX9957D++9915Me/fu3YupU6eq75dccgnOPfdcXH755eoYAR9SUVGRxbRjSEQiaETsKFTYdmn2EgonI/M3rluT1obckJBVpGhiEhV+6aWX8N577+H999+Pec94FI+R4tGUKVNwzjnn4Ic//KElpiYF6+FSn6jsK5dxULrLVBi9Tp8EC2SAUWo6vdKtnPDUkOPHj1dtePXVV7F169YYlC6eKXn33Xer4OWePXtQV1dnYXzpb+htk7E2tpPvyiA00TA62Rz45ubmXkGKRPTmm2/izjvvxHnnnYfVq1cDiDK39D1LSkosCczSLJTaicJJlsfTNTav5XvSfJbaweGIbgElN0t8+OGHlem3YsUKnH/++bjnnnuSZq7eKC8vD/n5+XjxxRfx/PPP45577lHzQW/7kaA+ocGAbikov1P7yBeOF6yVmkIyEs0T6RfxOUTeSLW1tWpjdF4rP3OSp6am4uqrr8amTZvw6quvwu/3Y8GCBbjkkkssGogMrifYSqie7eOfBEqYRcG20ixzuVwWDUZyOp0oKChQWjQSiS7dl+Tz+bBy5Ur813/9F37yk59g0qRJMffh9Wy/zCKR0DcTp2WlLhnElpqL3+VqBinsCEQAwO7du7Fv3z6sXr0aP//5zy2ADik7O1vV+JD+uM0WLektM24kzZw5Ey+99JKlwpcMTscb+8OlPsNgnGwcNJpcHDiZywd0M5McUD2pkyaJDq1Seur+FGCF3qUU53XSHyEFg0Fs375dJbzqpqT0qei4c5cXkoS3yZhM5TLNaLZFbm4uLr74Yvzzn/+E3+/HqFGjlN+akZGhqlDRRKqpqVEMXllZibq6OhUfmzt3LiZMmBAD5TMUIYPHciU2NwAkIMVrKBBpAcjMGLZfAljyXWUfd3R04Ac/+IGlFoZhGCgsLFQmbH5+vlrfp/u8DQ0NaGtrQ21tLWpray339vl86OzsVJWupKkqASUgtqitTskyYZ9hMDKSLMHFQeOg63EYKXnINNJnoGkipaUEFvTAsHRiJXPs2bNHOc1k9jPPPBPvvvsuWltbEQ6HsXLlSqSlpWHo0KFIS0uzbJjA65jTx+PMLgG6t7UlUkg/xjAMtYmf3R4tOXfaaafBNE1LVob0f+jvpKenq/St0tJS+P1+rFmzBo2NjQiHw5ZJTJImnJx4nMD0m7h2jEJA7snF8ZJ+MLNTACgBSiBFTuZIJKLa5XA4kJubi3HjxsHj8VjSzjj+7C+CTAUFBcjKykJpaSlCoRDWrFmD5uZmdHZ2YtmyZfj617+O559/XqGMEu1k/0nGlRrymAU5AGttDDKGjEVwIsnEVbnZNk0YXQpJBuVz9MwPktSOdrsdb775JhYvXoy1a9daVrfGI9M0UVFRgaKiIoRCIZV+o+/ewkknN6rTl+bTfA0EAmhvb1fneL1elcbDNB/DMJTWpwChKUpmo2nrdDoxY8YMVFRUYN26dTHvQOHGSSf7RJq3RDTJZHIDCD39TA+lyOrJPQXPAWD8+PEoKysDgJitbeXY0fSkP8uQgM1mw0knnaSCyQCwYsUKXHfddXj55ZdVYdNks3oOhfoMg0mVTR9GJvlKcxDo3v6UoIA0JWWaDe+rm5e8h94GwzBQWVmJq666CrW1tQnt+XgUCoVQWFiIhoYGNDY2IjMzE5mZmWojQAm8UHoygE4pTGHQ3NyM1tZWlT9Ik5hLbWy27hoS7DN5b8msNPPoS40YMQIlJSXYtGmTgrZlypmMPZJoXVDrSv+WiQCJ1mjx+dLklwBKenq6ejYQrffBTBIKUDKu3EyP80JmxnMZCv1Fm82GAQMGoKysDNu3b8e2bduwbNkyXHbZZXj55ZdRUlJiWQp0pKlPMBilImCNmssFcRxQyUjsdAYueT1J+gNkUJqR8dR8Z2cnHnvsMfzrX//Czp07D+odcnNzcd5558HpdCIvLw8HDhxAVVWVWhpDLSLNL6lpyYCE5xsaGhRzcULKwpiEstkXMtANdDv9fA77igxtGIYq7V1bW4thw4ahf//+qo9k2QQZgpCaR2pfLg+SloTUDGwfxxvoRhiHDRsGm82GJUuWYODAgZgwYYJlJYHUnrxehg6kfx4Ohy2bw8sA/ciRIxGJRLBjxw6sXLkS11xzDZ555hmUlpYmnBOJKFmksU8wmA4HS9udNrp0vCmJdftYSlVKXLnWSpa/jpf+8j//8z+WYjqU/mVlZejXr5+S0CQ5sZ1Op9r3y+VyITs7G3a7XW2yR7+ICBaJ0p7l5mjSMtUHgKqbQT8mnp+QCJGUwWr2KfPxDMPAqFGjMGzYMOTm5ipLgKlZ8QSbDEDzfjqoIzM42MeyVINEf6k5Ro0ahby8PLVamRursw1kZJktIttI01+PU0YiEZWT2NnZiYEDB2LXrl0Ih8NYtGgR1q5dq8zQo0F9gsHoPwDdFZ9kJrdU31ISSpOIxxIhg7p0MgwjBsYmcxmGgf79+6O8vNySZ8i26ClElMTt7e1qMNPS0lTpuEAggF27diEQCCA7O9uy3N40o+vOuAwmLS0NI0aMUNnhMr4kY34yR0/vD+mLSg3DviEYQeZ2OByWtVF8hgSI2F45qaUJSObXNSX7U55PIUeNw/bm5eWhpaXFstBThggo0KTw4DyRlokUsnQjWJPeZrNhyJAh2LZtGwDgtttuU9aKZE7ZVjlnDhbC7xMM1t7ejsrKSgwaNMgisXToGrAyFf/kAOgZ75TmcsJxArGTJdlsNowcORKDBw9WzyeaJ9smJwzQvYMjs7SZ4e3xeNDY2IjNmzerDG89fOD1ei3+ZWNjI04//XRl2ughBv6X703S/SYZrGcsTmoducpaakj2hSS53k0+R2ajyDZKRmR7mJHC9XOpqamqqpf0KUOhkGobx0H6eBwPud5MuhmGYah0KomC0ifbu3ev2qD+iSeewHe+8x3LeErEUBfQBxOI7jWTwzCMZwzDqDMMY4M49jPDMKoNw1jT9XeB+O1/DMPYYRjGVsMwzk2mEaZpYtGiRQqOlj6ZzBSQa6LkRJCmm5TmRJUk0aFtbW2NQY8KCgpw/vnno3///gqpk5WW2DbmSMo9kOmDUHNxwtTV1WHp0qUqdYc10tva2tDa2oqWlhYLcwHRWuxcs6bDw9QWMhczXn/q17Ht1Ag8j5Jdxt9YZoAMyhijRGWltpC/k6kk45Ox5WYPbL/cFJHIKseOcD41EVPGgO4FtdyIgX6mzIrR16dRu3o8HhQWFgKIavN58+ahrq4uJiZ3JCiZVKnnAJwX5/jvTNOc0PX3bwAwDGM0gCsBjOm65knDMJKCZyKRiEWjcDLFc4zZEXJVs7wPkS76XHoA1GazxaCDJSUlmDhxIhwOhwIW5P3kMzlQcsLJyZ+amor09HTlpE+ePFktJkyWBg0aZKmlzntLCFrXNPK7BDM4aSXwofctP3MVMy0H2XfSSuB1OjNxMvO7BKnILITU7XY76uvrLUwrtZH0pSnk7Pbomi6PxwOge084nicRTNkGKWgjkQjGjRunvn/22WdYuHDhQcW6kjUVe2Uw0zTnA0h2QdElAF4yTTNomuYuRHdZmdrLNYqqqqpU/QwJcOgIknSS5SSXE0EyHv/zus7OTgwZMgQ33XQTCgoKUFJSggkTJqglK1z1SjhaMjNg3Z2SeYfx/Au2MS0tLYbBiouLMXbsWIwdOxZ///vf8cADD1h+Lysrs4AW0tejdGebdE2nCwfpt5HpZA6kTBny+/1oa2tTkDdBDT1bg20hMCN9HvaztCZkqIQCqbOzEwcOHFAMx3NlOW/2Le8h9/fifKDG4vtQwPJP3kP6+ASRgGhytA5iyb7UKVlNdzjJvt8xDGNdlwmZ03Us0RayMWQYxmzDMFYYhrGCx1paWrBz506L78GgJjd0o3MPdEtuTjgdjZTOvhxwu92OkpISjBw5EpdddhmmTZumBo3pP8z9k7Er0Xbl10jpKic9B4vCYdy4cRaf5qabbsLHH3+MDz/8EKeddhoyMzMtz6AkluibNGE4YfS2keT5ktllojT/k8HIfC0tLRaAgYJDNz1lLiKhfaKEsp+keUoT3TSjJcBZ4JXalZ+lrywZiH/UZDQxdSBELjshestxcjqdyMnJwfDhw1V//eIXvziomGeydKgM9kcAQwBMQHT/5d8e7A1M03zKNM0pplaNZ9u2bdi/f78acA6olOKUTDIZmPEPv99vKYBClIlmXygUQm5uLrKyslTVKNrrjKfR4ZfP72pzzHvIwC/bKCe/BGvGjRunJt9f/vIXrF27FpFItCz0o48+qu7JdCtOEJk3SSaOhx5KwULGcTgc6l7SvGVbpcag1kpLS4Pf71fBfonAyWvITDQL5YpzakS2nwJTFiVKS0uD2+22CAqCStRgckEn0A3Tc7dQMo707WT/U0jrISAZ54szN5Ocxb3TITGYaZq1pmmGTdOMAPgLus3Aw9pCFoh23L59+9Da2gqg2wmmNJZoEIAY6R4KhdTkMIzosn/GVsLhMPbv36+KsFRVVcHv9ysGkLY6TQuJVvK5EmjgYEip2dVHqt08PmjQIPTr1w9AtHDl7NmzEQpFq+pWVVWpPvB4PIqhZDa6TMQFENMmnkdmofZhcm68NstnRCLR9LOysjIVnqCPo2+CIc1XPoumNbU728Pj/M6ajeXl5fB4PJblSIFAQIUn+I58P55HP7GzsxONjY3w+XzKmqAwJVpLRtdBsUTMJcdOR2T1Y8nQIcH0Rtf+zF1fvw6ACONbAF40DONRAKUAhgFYlux9WSps06ZNcDgcGDp0qNrUQOa4kdF0kg4qJa/NZlOpNDU1NVi2bBlWr16NSy65ROX5ceAk83JCxnsOj0k/j0wqA7E6UaLK+7B4qqR4oAUnKI/J9B5OIAmG6HAz+0WH2KlhOAEdDgfq6uqwdu1aZGdnY9iwYQpZlGUPdJLaTWpeto++EVceZGRk4IwzzkBTU5PFhJWCRO8r9rc0HTs6OpR5yvcls/E6mvs0+YH4RY6OBh3qFrIzDcOYAMAEsBvAzQBgmuZGwzBeAbAJQAjAHNM0k9qSwzAMPPvss/jRj36E3bt3Y+3atTBNU8XGyCw0GXS4WE7wrrYo36y1tRXz589Xu6N0dHTg73//Oy666CJL4JITm9kO0jTlPXkM6M7kkMiZHoPjhCbjTZgwAfv27UNHRweqqqrw6KOPYvbs2TH9wfN1lI6fyfwyh5CMqNdhlJNVMpSMa7G9u3fvxrJlyxCJRLB3715s2LAB06dPR0FBgfKHue+xDPiynSSajAxXNDY2oqmpCcuXL7e8y4gRI1TOIIvhSNBIkgwZ2O12teKbicYyo4TvKU1BakHODT1+KK0OncEPlQ51C9m/9nD+LwH88lAaM378eDz55JO44YYbUFNTg3Xr1qGtrQ3l5eUoKipSsDeZTULApEgkAp/Pp2p5rF+/HvX19TEOrETeKO3YsZJJ5ASUgyA1G3+XcR8ZVpAQstZXypfU2yYhZgkSyDbI/D8eo6aXgIb+Lom0kGEY2LRpk+U9wuHoyuLi4mKMHDkSLpdLLVOR/qHU9nxnlhZYtWoV6urqYqokL1q0CK2trRg3bpwylWXfyDoe7BcJwZOJaYXopqjsI/rXLJwqBaV8ps7YusY/WOoTmRwkmy26D/FLL72Eb33rW6iursbOnTtRWVmJrKwsjBkzRpkA+vqgzs5OtdFBOBxGU1MTKioqeqzNwIlkt9stizVpIlIq62ECoJuJpJkor5XnyiwDWR+dlOg7Jy0nFpmMWpxggMy3NM3uAjzyvhK9k6uVJW3cuFGt6rbZbCgpKUF1dTUCgYBaaWwYBoYMGaLifAR5ZPCaQfi1a9cqszDRJK2ursakSZPg9/vh8/kUsCGD6exT3iM1NRU+nw9paWlqVTqLIenvxHO9Xq+6nn4iEeN4JMf8cKhPMRgd5TFjxuCZZ57BzTffjN27d6OzsxP19fWWLUwLCwstS/7D4bBawRuPrr76amzfvt2yE6MelwGsk10GZ3UTiNJbSkOpIaSWkLEoh8OBAQMGqC1vV69ejepqKw7EtkgNJM1SaQozVsc/CWRITcjjNC9l/iHPkUBAeno6Hn/8cXzyySd4+umn1do2AJadLQ+GXC4Xrr/+enR2duKZZ55BKBTd56ympgZ5eXnKV6L5SnOOQpJ9SCFKBFLWdOEYUgDKPFKixaz5Ifv9oosuUveRTHq4iGKfKHpDkvGYKVOm4MUXX8TAgQMtjESqq6vD7t271V9lZWVc5iouLsb3v/993HPPPZg+fbrlN0pK6avIDAfG4wgiyCAtJ6tMx5I+mkQbSRys8vJuoHXt2rVoaWnBd7/7XXVsy5YtysSlxgKsWwYB3VpJakqgGyWT0LfUhjQlddNV9l9ubi4mTJiAH/7wh3j88cdRWloas34uWSoqKsJNN92EN954A9/73vcwe/Zsi0avqalR/c+MfloONFPJXBQcDBITvZVmo0QgGSyXvhiF0f79+1UbL7zwwrga7XC1WJ/SYFIKG4aBMWPGYOXKlXj55Zfx3nvvWSog9UYDBw7EGWecgXvuuUdlhug+ifRtJAomnWSaX5FIxLKoD4AaNB3+lmgmE2plsFSXijTHSFzJTLheDrK8NwUD/+vHyEzSHJSQPONiDocDzc3NSqsCwJlnnomXX35Zff/Od76DRx99FPX19Un1v81mwze/+U2kpKTgjjvuUNsk0bTlBhAAlI/M3SXpBkjLQGZq0BfnZ7khH5/Nzwy3UHBycahkriFDhmDQoEFHDNiQ1OcYTHYOEO2s//qv/8J5552Hq666SoEbjzzyiMpQp+S9//77UVxcDMMwUFRUhFGjRsW9J0mmPsmUID3WBnQ72ED3AkQZq9LjVXLSE+0CogwiTayzzz4bI0aMwKBBgzBlyhSsWBFNbNm4cSNyc3MtfqCuIeOZM/I8tltqOGoxfYGmvtT/ueeeS3rcrrzySpx77rkWS8MwDEyfPt3im7Ff0tLScNlllykGY2zS7XarDS9k/JN9L5FaGRsjSSEn34nLc2Q8TwqTyZMnY/z48UecuYA+yGCSISSik5WVhbPOOgumGa19MXv2bJimiWeeeUZttv3AAw/EdNLw4cPxve99D6FQKGZ5isxEkLmPcnLKdUYcQJkNIc1JafvLPyJYZD6JpvXr1w/5+fkIhUIoKChQx/Use11DStCC/SV3YuF5UkPLPb2ouailFy1adMjj9s9//hOLFy/G/fffj5NOOikm71L2KTWPbk2QURgLpdaR/Swz8HWflBYIwSr+RuaSyQRtbW2WeNjYsWOPivYC+hiDkTgpJPjAmMavfvUrfP7553GL0MjOJW3YsEFt8JboWTJLBOiezJSY/C5jTXLyy+uAbq2hT3wd4eLzybyXX345PvvsM5XuxTVy+no3eS+2i8+Ufob+uzSTZfskEnooFA6HsWfPHlx77bU455xzcO655+Kqq65SzEvggjE6m82G4cOHY9q0aUo4btmyBVOnToXDES28QyCCJNPF9EA8rQ8yG0sv8FwpMN1uN7Zt26Z+z8zMxE033RTD8CTZL/HQ5N6ozzGYDlGTWlpacP/99+PZZ5/tMc3lYJ7T1tamtAYHR+awyewCGRzVzU4pTXlvag9qQTKpz+dT5zEtiVpu5syZeOihh3D77bejs7MTy5cvR3Z2NnJyclS2CUkGx+XzpFlFgIQTXE50rmhub29Heno6vvGNb+Bf//qXaitjXgDUs6nNgShTbd++HfX19fD5fEqrf/jhhyqof+ONN6prWIWKbSorK8MTTzyB2267DcuWLUNFRQXC4TAmT54Mt9uN9PR0+P1++P1+CwAlM+yZNkWmJZjBVCuplSiUKioq1NZVRUVFePrpp1X5hHhzJBEjJSuQ+hSDxYvbdHR04J133sG7776LV155Jeaa0tJSZGdnW5Ay6YO0trZa8vzks9avX48zzjjDArVLbSXNPsAaqJW+kJ7eo0P48pkbN25UzFFcXIwbbrjBghJOmDABkydPxsqVKxEOh9XuLQzu2mw2tQJYal3ZfxIokmYvLQIyTltbm/JtwuEwTjzxxJi9q/WsFaB7smZnZ6uMlJqaGuzduxdAFBn82c9+hpSUFNx4440KKJKJuw6HAzk5OfjhD3+oNjrfvXu32pKXvpIEOyTaSRifDCCFI9tIy4F7d2/fvl1lkuTm5uKxxx7D9OnT4waY2Y+HS32KwQCrFgiFQnjttddw1113qRLKaWlpKC0txYABA5Ceno7MzEw1mSSMzMHgRubBYBDV1dUqcJqRkYFTTz3VsvIViDUDiLZJjUBUSyJzvEbWbQ+Hu6tYSUedFA6H8dxzz+Ef//iH5f0lUrd582aEQiG1Z5dEzHSNy+uJwhElJLwPRDfaCIfD8Pl8CAaDqvZHZ2en2oZJwv1MrOUkJFBCgZKamor+/furPbq2bNmC+vp6RCIRPPDAA/B4PLjyyist6U9st81mw7BhwzB79mw8++yz6OzsxNKlSzFr1ixVciEtLQ1tbW2KSRkk5zjRXI9EIpZ91Ehc6rRlyxasWrVKza8//OEPOP3002OSqklHyh/rM5ugG4aB7du3o6ioSEniN998E9ddd53qsNzcXEyfPh3Z2dmWwpgyY5rHaNZwsrNsMvfipTNeW1urmEb6MnIZhlywB3SvGJaSlczG+BWzLLjkA4huFEGf42Bp6NChmDBhgspAkAFZoNuhJ/E95LooJk2zwE52drYKNTADBOgOXvNddQ3P55IZ2f/MlF+2bJnaR/nCCy/E//7v/8Ltdlt8J/Ypmfj3v/+92rssNzcXp512mqWGPH0rprylpaWpd2NqnBQ46enpcLvdME0T27dvx7Jl3Tnn06ZNwzPPPIOSkpIYv05aIXHmqTpeU1ODQYMGca4eO5ugcyK/8cYbmDt3rtIUWVlZKumUwEM4HF0DxsRcnivTc2w2myqjxvro48ePh8/nUyuYOVkkMMBJRN+D58lJLc1AmbENdPtIEvHasmXLIffLjh074HA4MGHCBDQ3NytTjgmy3JdZanEKCZqH7e3tOHDgAILBIIqKiix1L6gZqdGlmcx7yURaAEqjUIiQIaZMmYJVq1ahtrYW7777LhwOB37/+99bVhCTGdje2267DYFAAC+88AIaGxvx+eefIy8vDxMmTFAa2TAM5OTkqGdTyDDTnxA/zcf6+nqsWbMGDQ0N6rmTJ0/Gn/70JxQVFcWYl/FAqER0TIMcr7/+Om688UaV1T5t2jSUlpYqqcVArNziB4BlgnAAHA6HyqJnrUG73Y62tjY0NDTA4/Ggvb1dDYwetJTJunqdCxLNJRmf0jc58Pv9yMjIiNktxO12qzJuUnCEw+GYrV/37duHsrIyrFmzBgMGDEBeXh6ysrJULUau3KVWaG9vt5hmzN8jaKJX6JWQv5z8EvCh/6XHFtn+1NRUZGZmYvr06Vi0aBHq6urw5ptvAgB+9atfITc3V70nn2EYBtxuN+666y5UVFRg/vz5aGpqQlNTE2w2G6ZOnYqWlhaV5cFFobKoDS0V9v0XX3yB/fv3q3EqLS3F1KlT8cgjjyAnJyfG8tCZK15wX5+nyVCfYjDa14888ojSEvn5+SgvL1eON2MkACzrk1JTU+HxeCwgh91uV0wIQCFmdrsdWVlZqrJTeno6mpubVW4bn0MzUF/OAsDiW+nJvXa7Xe3gwWUVKSkpOPvss/Hee+9ZkMvs7GxkZWWpCcyaioZhqO1OvV4vqqqqMGHCBGzatAkNDQ1oaGhAYWEhUlJSUFRUBI/HY6lJSKqpqYHX64Xdbkd+fj5OPvlkeL1eeL1eC4hBX4tmpVwFLNE4GdzWMyfYZxQsQ4cORV1dHYBuc3/y5MmWlCSioF6vFz/96U9jNqfftm0bxowZg6ysLNWnNL+5Ip2ac8eOHcrXlZkabrcbTzzxBGbMmBEjCCX1ZB4eKvUpH2zVqlX44x//iL/+9a8Ih8PIycnBueeeq5hL2u3S/9ERL6B7kwcAKttDD1B2dHTA7/erPbeoweir8J4ys4OTSjrXNC/C4bAafGoIak+Px6PgY5qTvKdcdsJnyIRdrtJ2uVzYtWuXZUkJYDUFRZ8CsC4KNQwDI0aMUBWVJDgjY35yTR3bJQPoJAoymsi8nkwTDoexfPlyVdB14MCBWLBgAYBuARUIBPDAAw/gX//6V4x2t9vtmDhxIoYNG2Z5F2ox1uyvra3FF198EaPxnU4nTjjhBPz4xz/GySefbAlWy3eR86YnBjvmfbDNmzfjqaeeUt+5b7PMoiYDyIEEuoPDOtxOiSoX20lzjOubPB4Pdu/ejYyMDEQiEWRmZqp7SRNUMpMe1JVlsbnQ02azKYblJKTAkMxOZqOJSuHB7AuigiNGjEAkErGkWyUbFzRNUxW04aJJaarJjAuZJZIouKpPRtZKkdsJ9e/fH9XV1ejs7ERtbS3efvttXHTRRQpA+uUvf4lnn33Wcl+Px4P+/fvD5XJh4MCBMeveKGDpZ+sZHABwxhln4IILLrCEQXRlIqH5I6m1JPUZBjNNE3fccYf63r9/fxQXF6sO9Pl8ynQjg+kxKT2rgd9p8hC0kA4tNYZhGCgtLVXoFwDFzHIgJJoIdJfypu3vcDgQDAZRV1cHp9OJ3NxcZdayHQ6HA36/34JOchIEAgHFTDKOQ2bz+XwoLS3F9u3b42au6JSfn4+GhgY1uVj/nW3X8z5lPqauGWVQW0p9TnAuhpXmeUFBATIyMtDY2Ai/34+3334bZ511Fvx+Px555BE8//zzAKD8sJEjR6KkpERV2ZLak88n0EQNaBgGZsyYobJgAGDdunXo6OjA17/+dRQUFMTEuXoKIh9J6jMmon5s6NChOOWUU5QpRyZgMRPJGOI+lhiJlLAsaS0loYwVhUIhpKenq2h/c3MziouL1RohZnAD3aaVXDIfDncXO2VdwdLSUovWkgyhb5Cg+3v6+8hN230+H+rq6rBmzRqV8tOvXz9ceOGFFjPWNE187Wtfw+WXX67KSI8dO9ZSdFP6j3osTUL3QOx2T3yGLuR4T8auGhoaVGIvAPzpT3/CF198gb/97W8AokzP2pQSeJD35B9N7HhCsrGxEbt371al/wBgxowZ+Mtf/oLS0lJlefCe8p2l6ZiIpLY7Jk1EktPpxMiRIwF0d7IM6sqsBD14KbUX42FSMhPa5WRh4JTmTWNjI5YuXYpQKIQ9e/YgPT0dw4YNU+2QgVgijwQX9uzZg7S0NEycODFmsrHtst4i34/vw3fX4XEJRKSkpMDtdqO0tBSGYWD58uUIh8Pwer0YO3YsLrvsMktf/OIXv1Dty8jIwJAhQwDEQtNyEsuwBJFHJuFKuF5HUnlfvrvMoZT0yCOPqA0XsrOzcfLJJyMzM9NyrVwMStOZ4RegW/jY7XYFCtntduTl5SEzMxNr1qxBJBLBwoULcd111+Htt99Wgkr6pGwz50sizRbP90yG+iSD2e12y37DrGlIs4XxKAkb00SJZ1PLRZFysKW2IJz/wQcfqHMIFe/bty+pdrPTHQ6HqjPBILPdbldBaJksTClKAEYWauEkl0nPzEJJSUlBQUEBxo0bp7ZJ/f73v4+TTz4ZZWVlqr+2bdtmAWZobvIZElHjXygUUnVPpAWh+568lqauDtawv3XNx6Uiubm5OOOMM1SbGNfkmEpUVI61XC3AEAjLabNqc2pqKlauXInOzk6sW7cOK1euxLRp01SbvgzzEOhjK5pJw4cPV5ONJhprP7BWnyyNLJePSF9JagfJTAwey9XMErXTSd6rpz/S1q1b1cI+ORnkamTGrKTpyXQfTnwCM1Jb0B+lsMnPz1fLQ8LhMJ588klL7qH+PnxX/tcDyexz+jIej0dpDyKzetyRYxWv/yORiAItJGVnZ+OUU06By+VSFog0vel/Sr+V4AmfRQApIyPD4jK43W4MHz4ckyZNAhA1qW+66SZVLevLYi6gjzJYYWFhjHaScDOzBuiXMIXG6/WqoqN0hAnRcwDlvs7UDMwE0Du+f//+GDNmzEG3n7EeiWZysnMy6j4PAQxZipoTTraZ5ho1RXp6OvLz89WzP/roI1RWVsY1zYDYzbylCSoZkqELmojccJ1aRgosmt+8v9S4QJR5ZcmB7OxsywJNMg/zBlk4lOXS5dg4nU5kZWUhMzNT5SpS6DIXke0oKiqC2+0GAFRWVuLDDz+0JBF8GdRnTUSagnJLG0pIDigHhTY48/F4HSePdNqB6MTy+/0qf40VifRilDfccAMuuugivPHGG+o6PoffI5EIWlpa8Pjjj8e8AzURTSceB6zLTXRnHbCasnyu3CpX+iCjR49Wuzbu2bMHL730EubOnRszkYju6SEOiQhKf09OXloQHA/ug0ytJjWh1Lp8/yFDhsDj8WD16tWYMWOGZW0Ytb0su8bxokbjmFMgSguE8UWuvmAbsrKycMopp6itsR599FHk5ubitttuS3ImHj71SQYDrNVzWTudCCLQXSWIy8sZeGSZLmorSjUOHM0NpmFJSawvmweiCNfs2bNjIv+c9J2dnbjqqqvU8ZKSEpSXl6timpyM0okGusEMwLotkL4wUwdx6OtwfyxOdJfLBa/XCyC6gZ/czI5EZmZ2i5y87FMSk6UJtzOtSraB/cAAPRme7ylBp0gkWmS0oKBAPTs9Pd3iG7OkOeOV1NKyHr808dgWFh6VoBbfJScnR/VNJBLBv//9b9x6660WgINjKc383oLNcox6oj5pIlLSSnRN1i+32aKripkxzeBsc3OzyrYmkECzgwwnd+Ng3IadqTvjMvCqMxfbaZomamtr1W+c9NLE47Nlhj+lLNC9EpsOfLz0K/5Ov0SHmk844QR13vPPPx83sZiMwXBAe3u7epaEwAlaSECDDMNJT60lN94j+kctJzNCTNNUY8P70K/juUwGZhv5TAloyfimzOpnjURptXA85B7MVVVVWL16teX+0v1IlpI9v08ymISwGafSHXY9RYomDH0ESjNOQPpskUgE7e3tKmFYSlp9TVBvFA8UkaYoJ6i+hkmaVABU+/Q0pXgSlWlTXEWcyGmPNwEkymmaZkxIgM+RQXtOfkLl1IJkdJ3Z5d5l0gQlgMPvvIfcy5rXSJ+VDK4nFvA+EpwhCEazlm7FoEGDVB/t3r0bX3zxRUz/HCyDJUvJbCFbbhjGp4ZhbDIMY6NhGN/rOp5rGMaHhmFs7/qf03XcMAzjMSO6jew6wzAmHWyjlixZYkHSqMZl8RKaC2RAmmR6kmowGFQmDrPL9XM4QWQhyoKCAsvA6NQbSsf7UiuybXItF30QTni+c7xQg4zruVwuZGRkWCZXIpo8ebIyZ4LBoNpsgX0nTSSJ5Ml3YxulKStNOBmAJ7wPwJLyRXSU76X3qwwT6L/rQIxsk2wvNZoeRkiGjhaymMzTQwDuME1zNICTAMwxolvF3gPgY9M0hwH4uOs7AJyP6K4qwwDMRnQvsYMiai6g2+QiYqijb3LQZOBRBis5oSVQYrPZ4Ha71QQxTWs5tREjRuDkk0+2THb5bAmbS+knQQp9EnCw5WaBbB81hIS7JfzNd+3s7FQanRNKrlGTfWgYBq655hoFq/t8PlRVVSmfVAZVOTGluURtKk1I9oVOMghMhpA5gLKf+Ds1jdxeSYIv7BtSvDGgFSGBMAJdFGB6m2Wq1dHSXKr9vZ1gmmaNaZqruj63AdiM6K6VlwB4vuu05wFc2vX5EgAvmFFaAiDbMIwSHASFw9HSWlT7hKblRCIT6j6LlLokSlIm3TJtSjrhOogBWLPNJUMB3YU/9QEyumB1SlUiX5JRpe0vQQZC4ZIZ+bt8rtzooCcJTQ0zdOhQday9vd2yrESasJJJKRh0c1FaDdI/5moHamGp2SORiCXeJzUOAKX1ZFqZJGkSUliwvfrYs28kIul0Oi1VnX/zm99g7969ln49WnRQPphhGAMBTASwFECR2b1H2H4ARV2fk9pG1oizhSwpGAxiw4YNCgJm58s4lw4CSH9AX9UrNQnQbWZQapqmiX379ikn3G6PllCTDKZrsp5IrqyW18hEZGpaml4+nw8+n0/F8xjXkQKFbZaxJumjaP0LhyO6ofucOXPU8d27d6O+vt6CovHe9J2k2S2PSXOabZL+DpOwOTZkOK7Vk2Ngt0fXzElggveWsUAe17W4TPNin0gGl4KL/S3nl/SJ4wnXnuhgmDJpmN4wDA+A1wB83zTNVs0/MI04Cbu9NPIpAE913dsEolWWuFCOA8xl4DT3ZL0IqUF05I0movRxpPPtcDhUXlogEEBtba1KiA2Hw3j44Yfx299ad8aVUO4NN9yg9lbWEUbGwFgKms4/NQZgreWn+2x8Vwnt65pTJqfKFDGSzBmcMWMGzj//fLz33nsAgFWrVqGwsBAej0dZCkTjZNiCZiSZi2lM+vtScHBBJEMTBJuys7PhcEQ39pPXS2tD9zdlwJgaUE8b4/PpHvBeMkE5XlwxHh0tTZYUgxmGkYIoc/3dNM1/dR2uNbp2uuwyAeu6jh/SNrKGYeCBBx5QRUL37t2L2tpalJeXq5dncFKiTXTMObASbaMG1H0J3dwjg0mKV+pN0n333QcAOPnkk1UVKMOILnnhveWgyrQsqd3kRJJrqeI5+mQAHU0Nh8OWfMkpU6ao2iXhcBgvvfSSBcBpb2/Hnj17MGjQIBVD8vl8Fq3GtkmBwTZLC4FjICc10VzTjNaeHD58uAUZlmOhf5Z9xN+oJWVJCPYn+1n2Dwu3yvIOcjxnzJiBrKws9f1QEORkKZkdLg1EN9zbbJrmo+KntwBcC+A3Xf/fFMe/YxjGSwCmAWgRpmSPNGzYMFxxxRWq/uHKlSuRk5ODzMxMZUZJM086sJSiZDbGTvg7/RVew4FsbW2F3++PWQ2bLMmS06YZXdBI55pZBWwDjzOoSuKxSCSiNLQEBMhsbHM8v5A1CQFg4sSJ+PWvf61Sphj3IYVCIXVs4MCBSEtLQ1pamgVs0P0rHdHlxCYDSnAmGAyq9LXPP/8c69evx7hx41BYWGjRkBSUetk82S/ymERMdYRThkYIIkkm3LNnj7rvrFmzkJubaxlHKZyPJCWjwU4BcDWA9YZhrOk6di+ijPWKYRg3ANgD4Iqu3/4N4AIAOwD4AFyXbGNyc3Mxa9YsfPDBB2hpaUFLS4va2oZlv1jDD+iO55CZ9IkomUqikizcSZNn0KBBuPDCC/Hvf/8bHR0dyM3NVWlJkkGkVpJBbUlkMO6MwrZwMsltg6QElhkPchtVTlzGvGQaEk04HaZ/+umnezV5CNmXlpYiEomopF4yh/R92A98H6DbFJOT3zS7Vxl7vV589NFHSjtWVVVh0qRJGDVqVAzjEBiSVgi1mPStmIDMmKeMGQLdpevYl7qlotPRguYlJbOF7EIAiVpyVpzzTQBz4pzbe2McDlx44YVob2/H9773PUQiEaxYsQITJkzAsGHDlE/DXEMWiGHaFJ1zaRbyv5w0nPD0PxobG+FwRPckCwaDyMnJUQyt+zeUkM3NzfD5fNi/f78yv1JSUnDmmWda8gw5WXSnWzKXZH4ZcAWsG+wB8c3cbdu2WRDAeMim/B+JRDBgwACceeaZME1TbVyYkZGBnJwcS8UuPk8GlKX2khqF5tmqVatQX19v0VZAdMeY4cOHx2TgM4eQy3noE8rgsjThqKHk88mQ+uoDPXyhAx6J+qgnSgboIvWpXER29mmnnYbBgwdj586dCAQCaGtrUwFjZkfTjAmFonv7ylXOevCWZg/NRxmYBqIlpCORiNroW88HlETpmZaWhnA4jNLSUowZMwZNTU0oLy9HSUkJamtrVZoWE1k5aWTBHrZNxrrYRmk26oip9JUikQiqq6tjmConJ0cJh4kTJ1om1tatWzF27Fg0NjYiMzMTJSUlaGpqUhWQMzMzkZaWpqphSZNR12wAVPGgpqYmSx3CzMxMjBw5EmvWrFGam/uAyfEGusMCZEpqU+mTMS8RsPpt7FeZiCCtjYULF6pzTzzxRFxzzTVxGUQHWvRjh0J9isE4CUpKSnDdddfhJz/5CYCo5HM4HBg2bBja2trg8XjgdrstSahyL2ZqAU4EyVx+vx9tbW0qI5uQrayzp8O/UiIC3eaQlJ7cQK+hoUH5WHwG0M2Y8XZalMhivMWMugnI6+x2u6ovTxo7diwcDgdKSkpU3qUusceMGaP6gisKMjMz4XQ6VYk4QujUvlIIUFtRK1dWVqK6utri59jtdvz4xz/GOeecgwsvvBD79u1DR0cHNm/ebNk3DOiG+2VJBTIkhZFhRGvqc0xYckGCWoynEexgH+rxPZl/eqgMlCzq2GdqchiGga1bt6KgoADhcHQT87vvvhtvvPGG6uS0tDRMmTIFeXl5AKBiK9JB1ZEp+jVcb+X1elX+GzUcs/R1+F9OJsCaDyedffpW8pxgMIi0tDRkZWXFrJ3ixJT3l+ar/j6yRAFgLWdNtOyTTz7B4MGD1borKcl5D9le6RvSx6FZ3NzcrIr2ZGRkKASRz+X3Xbt2oaGhwVKr0maL1kO85ZZbcNFFFwEAli9fjttvv12Z4pMnT1abI5JR9JQq+qHyubI6lyybRyZiTiNDMkB09fSiRYuUdTB//nz1bBky0MMPYm7GfDcMA/v27Tv2anJIxzYnJwePPfYYOjs78c477ygUccmSJRg+fDhKS0uVna5v7UOiT0Z0jDY7N0AwDMPCpFIikmTakvSl+Dwep/aTjMY6fxkZGer9yJwAVHyP7ZcSPBG4Iic5J5phGDjzzDPVdexDahy5pk6SXGHg8/nQ2toKh8Oh6lq0trZiz5492Lx5c9IS+7//+7/xi1/8wtLuqVOn4qqrrsJjjz2GUCiE5cuXY+DAgZY6lSz5wIRsAJa4Ie8nqyVLkEPWXpFCb9euXep+s2bNUiurD8aPOhzqUwym2/Yulwu//vWvMXv2bMyZMweVlZXo6OjAhg0bsHfvXthsNgwcOBA5OTmWuBI1k2lGg8g7d+5ERkYGzj77bOXLUWLLeg8yBYimJZmH9r/MINBjQzRFCaiEQiE0NjaqrA2dKSVjSy0n7y99Sj6D68HS0tJUABuIzTiXCbsEhzix2E9cWEnzyu/3o7W1Vfm6FRUVvTKX2+3GkCFD8OCDD2LYsGGqPQcOHFB9dPrpp+PNN9/Enj17EIlEsHLlSpx00kkWYSA1KxFBglZOpxN+v19prmAwqFZ6002Q/rbNZsPChQsVAHX66afjoYceUlpOn3dHi/oUg5FkZxcVFaGgoAAvvvgiHnvsMXzwwQdobW1Fa2srAGDNmjVJ3bOtrQ2fffYZxo0bp8xNTkbpS0nTjFKVzEIzBegOwpIxeJ0saJOZmYnGxkY0NDQgKysLDocDHo/HUj5OInus3BQv5kXJHgwG0dLSorQPmVSWIZBaUvqfciJxEnM3SQa4+a4+nw+rVq2y+LY6nXLKKejfvz8uvfRSnH766crEe+WVVxAKhfDjH//YEu+TjHrgwAHU19cjPz9fMTNNaxku4HygFUKrBYDazENuVgFEBVNdXZ2qcZmSkoLzzz8fRUVFMSECfdx1f/twqU8yGGA1Fw3DwPDhw/Hoo4/inXfewfe//31l9hwMcUvW/Px8BSgQBOCzSNIHk6YoJyYQm/EgieggC49yp0b6FzRTyaAUKExqlmYnQYD29nY0NzfDZrPB4/HEOOoS+ZJLSthWaUbzubKcAc1fFr0pLy+3ZLg88MADGDhwoPo+YcIEtUOn3+/HW2+9hVdeeQUfffRRr2PR2tqKtrY2VbCGQWQZD6PAIeNIYUbfjd+lSXrgwAHMnz9fxTnvuecehRzKPzmGR4v6FMixY8cOFBcXqw7m5JbABQOOv/3tb7Fp0yZs2rQpJs0pEQ0ZMkRtZEdGkT4cfSiZOS6ZXJp00oSkGSSLiVLyezweFBYWYteuXdi/fz86OzuRm5uL1NRUhdQB3QzACc/7BgIBNDc3o76+Hunp6SgoKFDCQfqe9D+AbtOSE5PwtfQlydw0Z2kuGoahTLzOzk6sWLFCpRmNHDkSCxYsiJmUmzZtwlVXXYV9+/YlVW0YiDLnrFmz0NbWhpqaGtjtdks1X7ZTCgDmS9rtdsviTQAq7LJv3z7Mnz9fmdA/+tGPcMMNNygQS49rylhl11y0CNr/KJCDLyu/S9SLPlpKSgruu+8+hMNhLFiwIMZP4MTq6OjAww8/rMwUaQryflzSQC0l04E4CeUAk8E4kWhWyeRdZjkQ8aqsrMSnn36qQA8uYWf9Pg68jONFIhE0NDRg//79aGtrQ1NTEzweD0455RQ1EaXWlUmwEmWkdtWden6X+Y0yZ5MTsX///qipqUE4HEZ1dTXeeecdXHzxxQCiGrOiogK33nqrBaLn/ceMGWMBHvh8hlz27dsHp9OJvLw8+Hw+NDc3K0SXcUN9fsg+AmDJU2xra8OSJUsU891999248cYbLYtA9fsdbepTDEaSgyI/A92+Awfs1FNPxYwZM9Rkks79T3/6U4smGT16tJo4egaAzI6Qx+iTAN2pWfIzUUO5Spqgic1mQ1tbGz7++GNLQR063nV1dT3GY/SFlF6vF5988glmzZqlFpVKJpKZJ5T2Ev4HYte1EYHjUn8ubiXQk5ubi4yMDDQ3N6OtrQ3vvvsuZs6ciVAohHvuuQefffaZCiw7nU54PB5MnToVNlt0QSsnt/TlqIHb29tV4JlxyEAggJqaGlWSTV5DMIj+MS0Fr9eLjRs3orGxER0dHXC5XIq5ZNkICaLolOj44VKfZDAgfr0LoHtZim7aygnEz83Nzeo8aiuihTxPJg3rWpC/kcl0X0syKrWbXMXLz/GqVQFI2pySxH2U5cJD+e6SIaWElqCJtAj4m81mU2aUjMulpKRg4sSJ+PTTTwEAr776Kq688kr87W9/UxvrAcDgwYPRr18/FeCWzE2/Tk8AoL8ZDAbV3m0ulwvNzc0q2E0GoZlLvyoQCGDv3r0IhUKqDDfH5N5778Xs2bMtqxLiwfL6WMb7nOj8ZKlPMZiUyD2dI+3keP8lQCGvA6zl4OhYUyvKLApOXDKZnDDUTtQwEiSQoYZ4Jsgdd9yBN954A16vF/X19TFmkCRWzZI7vhQXF8ckIsuJnIikFpMMyWukoNBTtGg+sq133nmn2vPLZrNh5MiRGDNmjFqBLte3ycmtr3Bg8jIZmUhhWVkZ0tPT4fV6UVFRoTQ9r+G46MnWTqcT//M//6OYKx5iGI+S0XDy2MHgFn2KwSQdKvjCSbN7925UVnYvrOaevDoj6iuGAWtCLRlMmpBA9wpqWbcdiNUaOg0ePBiLFi2CzWbDgw8+qHaAjHf+6aefjhNPPBETJ060MA+RNuZUErKXTCTjc1LjU1vJCc7j1Np8FgGg/Px8DBkyBNu2bQMAxVwAMG7cOIwcOVKVwOY9pMaUx2TYQ27ax/ZzQ/P8/Hzs378fFRUVPYx2N51zzjk477zzcO2118YFLnT6MvwvoI8xWG/a62Dus27dOmzYsEEdGzZsmOpomZojkUGgWytwUshsAd2E5ATkd95DSsFEC/lsNhvuvfdeANYkXj1gLrdCle8nEUE5QWXqFe9DsIbalz6lznhsLxlAj8VJMgwD48ePx+jRoy3hC5q9EmHlXmiymKlkPLabqWUMdq9bty7+AKO71F1+fj4efvhhnHjiiWqNV0/J2vo99HE90tSnGOxIUU82NCcCSTcldZLBZmmKSYRRmiNyYgKxklICEvrk1tukZ3jwfnpcTj6b10lBwckswx8yBCFRR90v4z0yMzMV4mgY0UKnI0eOtCCuDHHIjBK9fyWjS3+VsTe324309HQEAgFMmzZN+X5AFNrPz8+HYRgYMmQIfvSjH8Fm6y59zjFOlrkk/Z8BOXrzJZIlvbNkp0utpXcsTRkeJ5ggM94B64DIMmD6ljs6xdNyJIli8n88x1wGuqW5JzWTDMLa7XYVb6NgYFYIUUTZJxQgsrrToEGDYLfbsXTpUowaNQojRoxQ95baVDKuvm8zE6slcCEZEejeJjg1NVUtpampiS6ILykpwTPPPGPpW52ZZJxSavGe6GgxF9AHK/vqSa2A1TzT7Wt5nTRpzjrrLJxxxhnq96VLl1oGW2aXy4C2LnVlsqk8Hq89ROIkELJ48WLVhrPPPhtf+9rXEpqNcqLKd5JkmqYFqqZGlIyhB+ipXQCotWwul0shc3yOzCjX0cZQKITBgwfjv//7vzF27FjL5CUKaLPZ1MpoKcDYZu6awvtRCzN4zH0H+H4ejwdFRUWqvz799FPs2LHDApzozCE1cCLrRPep9fjrkaQ+p8EOlWQA0jRNFVshsRagjC1JM003HYHYwpbyfIlQUZJzEhuGoUxLCdGzTb/73e9UxSVdWLBteXl5uPHGG2OY0TC604RoZunmq66ZyYQSkKEAYB/xXeVqcN6L7xcIBOB2u1V4gn0ifSmigsys4PUMHusTn5ObbZLPTUlJwahRo7B79240NzcjEAjgV7/6FZ5//vm4THG0mORw6JhjsHjxLzmZ5ITPzs62aCm95j1NIA6whOqBbiBDrnWSDAd0AyZAdwCVyJ2+UHL+/Pk455xzsGXLlh7heSCKFP7973/HrbfeGvO+fJZkKPmd7Za+IX0xaj72hfSJZP9KX0bX6FJL0JTkPagRpYUgVwLIZ0jfj+9Ef45jKjUbALWngMzF7MvU50zEZChRkFmPe9x7773weDwAolkQmzZtUkscpAkkGYbmI00piXxJ5FFfms4/ZhikpKSozAJSa2srNm7c2CtzAVDLcubMmROjWclQZCauDaNvxcku35GrsKndWUWYqwpkHRAJsEiQhQKD95KhAtmv9KHI1HqZ7njxScnkfB+CJieffLI6/9NPP1WLcI8m+tcbJcvc/zEaTIf446FJZCRqGJovenxMpvVwckhpLs06GS/TtcbOnTtVJSRJiXwwneIx4vbt21FeXm6Bovle0u+SbZNaXmo95ktSYNCMjEcsMATAorGB7rVwci9sLp6kxmNaEwsXkSSiKU1FtlMGuOU1OiDUV+mYY7BkiYM1fvx4fPHFFwC6S61JyJnnSsahhJYTU5p70n8gcYLR7GxtbVXoF5/BrXEnTJhgKSbDjHEp6YPBINauXYuGhgZLShUThmUGBkEDHtMr23LSSl8S6DYP+f66byorRrW3t6t91vQMEr6f1CoEPSRjS1NQ9rtspw7s6GZ2POoNfZXI9JcVYCb1KQaTqJfsCF3L8Fz5XUpnabvfcsstisH27duHtrY2ZGZmqjw4qYEkWMB78lkyo4OmDyc2J7Db7VbLP5xOJ2bOnImPP/4Ybrcb/fr1Q2lpqTo3PT0dNpvNslOkJLfbjenTp6O6uhp1dXUqe2Lw4MHKhGU7CHboQWbdLJMmIGANOeh9KRlGxtCkVpQxNhbPkRqHbeGYAFA1RPTjkgGYyygBm3jzJF6/SZJzKN6Y6vc7GG2YrHnap9aDbd++HUVFRRboGbCmLunHSPrv/Oz3+/Gzn/0ML7/8MoBoVv0FF1xg2eJIR9dI+nd5Xzk5AFgmDYOxLpcLjY2NCl2TZh1NJoItnKjMZpfvyc32qqqqcNppp6G5uRlAbKEWmU+o9a9FI0stRsRT+mFSyLB/Ojo6kJ2dbXkXaj3WMySzy6RqVqRyuVxISUlR29xKX1YCUOxj+V5+vx/bt2/HypUrAURXii9dulQtnJU+uNRi8bSVZLBEKGQ8RtOP1dTUJLUerM+DHNI36k0YyE7lZ5fLhfPPP1/VIvf7/aitrbXUegBiYXdeHwgEYnYaAWBBIHWQJRAIwOVywefzqa2XJGPJakkEByQz+3w+i5+Snp6OrKwsVeNd+jZERIHu8gAsZ8c20nzkc2W8jPtz6evJqJ0Nw0Bubi4KCwsta+X4WQaT2X7GtriFkCw8lJ6ebonL8XnsB6Kc8hqZkgZ017E8HNItooP15ZI9/3B2uPyZYRjVhmGs6fq7QFzzP0Z0h8uthmGce1At76KD1ay65JIT4dRTT8X//u//qhJkixcvRmVlpSpznZGRoRBDeb9IJKIYhJNFws2cVAzYhkLRfaKzs7Ph8/kAdG8gKBdkkgiKANYsf2pXmZHACc26izTFpH/Doj1se1tbm0I1eS77Sfp+8vl8RyKEkUgEbrcbBQUFlv6V/qrNZlPlyBlIpoZKS0tDZmYm7Ha72rKXBYBkGXQ57jKvk/6ozEucO3cuMjMzjwiKGE+TJUNHjMGQeIdLAPidaZoTuv7+3fXg0QCuBDAGwHkAnjQM46C2rtC11qEyG9C9Duz000/HY489hvz8fHR0dGDRokU4cOCAyurmWiTGbGjuSQ3GPbto9kjGIajh8XhUkFmiihLqJ5TtdrvhcrkUA5OZGaTle3DS02/z+/1wOBxqJS/NS7nvFd9dDWLIumke+1eu5GZbWUMyFArh888/x4svvqhKacuwBhmIG5AzHqab0bJPvV4vDMNQe6Ox3RQAEmyRKwEosIDokp1kwI9k5gf74WCZLNk5eTg7XCaiSwC8ZJpm0DTNXYhuAjE1qdZ0kTTRdEhc/6873vJamblts9lw+umn49FHH1UaYvny5ViwYAGam5vR3Nys6rNnZ2ertB5OHpfLhfT0dHg8HqSnp6t9ywKBABobG9He3g63220Jiko0kozudDqRnp5uKXRKv4vgBbUazRi/3680k9vtVszicDhQX1+vGJrL7eVGeHKzQvaZDpPLxGHuZb1u3TosXLgQBw4cQGNjI1566SUVvqAZxzayzdIfkhqIjEYrwe/3K+uAjEKTlH/st55MwUSoYW8kQY2DvVa/R290ODtcAtFtitYZhvGM0bUJOpLc4TJhg7QUmEN5eUkM+tLJPvXUU1VJ7mAwiP379+P999/HkiVL0NbWhtraWuzfv1/5MCwpLZdxtLa2oqGhAVu3bsW2bdvQ1NSEfv36qfqMEkiQQVkdiZMBajnYlPiyD6hVaGLJ4G51dTUaGxvVshBW5KVWAawTgmYtz6NP5/P5UFlZibVr16KiogJNTU3qmgMHDuDtt99GfX19zBIdjpGsEiwZTQbhyWQEmOQiVZqVkukMw7AUNSoqKkL//v0t86QnK0c3yxOdd7TAvsPZ4fKPAB4AYHb9/y2A6w/ifrMR3ST9oEmiYjrp0okDx+N/+tOf8PHHH8dcU11dDZfLhdNPP11t4yonABmiqqpKOdm1tbUqZ87tdqv69BKFI6hBoqnE32RsSraZx/WcPWqrlJQUBINBFBUVISMjA16vF83NzWpDPdbC57J53k/6bPTPaDpWVlZi5cqVFr/oG9/4Bt5//320t7fD5/OhoqICxcXFypSUQWI5HtLK0N8pLS3N4i/yT9Y1kdfJdX2TJk3CjBkzLM/pSQgfrbhXsoL/kHe4NE2zVvz+FwDvdH1NaodLM84WsnHOiYv09MZc/EzgYe/evdiwYQPuuusutLS0xKyxAqJVhLl4MC8vT0njzs5OLFmyBLt27QLQnektqbOzE5999hnOOOMMlJSUWCaJrqWA2B1K9MnIc+R/mUZkmt1btHZ0dMDj8eDdd99FeXk5cnJy4HQ6kZWVFYP6yT4kIsqajStWrIDf71f+z9ChQzFz5kzMnTsX559/Pm688UYA0Uq//fv3x5AhQ5SmIsAhA9x6tgzflyapDGJLAIZACYXPli1bVHDd5XLhV7/6lYUppcDojQ7XGpKUrMY75B0uja7tY7u+fh0AxcxbAF40DONRAKUAhgFYlnzTY0lqJWlmae2MmagdHR144oknMG/ePKxatSrmvm63WxXSzM/PR0pKCpqampSp4nQ6sWnTJrVUvieSy+J1kyTeBOjJrJHSm+8mVyXzfikpKThw4ACWL18On8+HrVu3AgCys7ORkZGhJivrsfO+1dXVyp/yer0xq6anTJmCxx57DP369QMQ3Y1lypQpWLFiBcLhMHbs2IGysjILWktNyL6TY0YwRbcmaHLLvZ+pnXkdY2w8n7G4g2WWI8lcB0OHs8PlNw3DmICoibgbwM0AYJrmRsMwXgGwCVEEco5pmr1ntyagniSFrsmkU+zz+fCzn/0ML774omXCO51OFBcXY+DAgbDZbMjIyFDP4BISoBt9pB/TWwWogQMHoqCgQElhtkVnNsmA8fIaeVxqOJKceDZbNL/P6/UqCU8iaEPSaxbKSSspOzsbTz75JMaOHYvi4mIAUcHRr18/nHnmmVizZg1CoRCqqqrQ0tKidrmRO9yQmbjeSwbq+UyZLS8FE2DNyNm3bx+2bNkCIJog8Mtf/jKm9ojsx0R0MMyln5vo+xEzEc3EO1z+u4drfgngl0m1QKNELyQHgP+lFuCkBKJm3L333qv2egaik7OsrAzDhw+PkYIyM0EGZTs6OpCTk4NJkyahpqbGsg+yTmQquc+V9LekNpIIGyckibA73022TT5HvndvFC/hWKdvf/vb+NrXvoaZM2eqCU7/0jAMzJkzB6+++ip27tyJUCiEdevW4dRTT1V+HsuvsW0MZUh0l+/G/zQPKSwYa6Rpv2DBAqVpv/71r+Oyyy6zmL2676cLpGRNR5Iu7Phdn3sHQ30qFzFZki8NdDMf40D33Xcf/vnPfwKITvKBAwdi0KBByM7OhtPptEhbDrKOsqWnp6s4WGlpKYqKilBSUoKKigrU19fHbZOMn8mULXlMmn66P8fYko7S6fU09BCEpL/85S9YtGgRXnvtNXXPtrY2yzlut1tplsmTJ+POO+/ESSedZEEu9VxB0zRx7733Ys6cOQgGg6ipqUFtbS0KCgpUfI4Tmv1JhqMFwPeNRLp39SSDMWxhGAba29uxceNGxVz9+vXD9ddfH1er93Xqc7mIJSUlMZWVpFkVLwYGRJ12r9eLn/70p3j11VdhmiYKCwtxyimnKLOC8S89NkSi5KQ0ZVYFGZem1Zo1a1BfX28Jfg4fPhxTp05VElpPPeoJrJEVofhHkEUypJSqPLZr1y4sWbJE3XvVqlWqNDcBiLvuusvyzvfddx/Kysosy010yFv2s0yzeu+993DXXXepHMszzzxTMRnBCfYXzUS2QwoDqYVYZsE0TbS2tmLNmjXKNBw6dCj++te/YvDgweo8HeRIpMGkL9gTSasoWQ1WU1ODgQMH8rdjozZ9sqpYTgKJ1NXX1yvNBUQHh0FUSlFujyPvJRlZ+lCcGG63W5V4DoVCmDRpEtra2rBgwQK1hc7gwYNjEDs54HLgdOamliITSKc+HgSu31sSGZb3+POf/wwAlmO//vWvLf7rtddeixkzZlgmlO7b8p0uuOACdHZ24jvf+Q5CoRAWLlyI0tJSnHDCCcjNzVXBbmomfUU4QRDZR1zq09LSgk2bNimwBgAuvfRSDB061FIaQe/Tvkx9isGSoUTMVVVVhRtuuAFAtONHjBiBsrIytWKXm7fJ9Jt4MC/vy+2RHA4HGhoakJGRYcndy8zMxJlnnom1a9di6tSpahdLSfEADsnIMocQiL/BuB6cliYng9iS/H4/Fi5ciJ/85CfYs2cPvF5vrwDN22+/DafTiSuvvBJXXHEFJk2aFJfBqJ0vvPBC2O12/PCHP0RjYyN27tyJ+vp6nHjiiSowzywNmrKyjgjHjUhvU1MTGhoasGzZMnR2dqp3uvTSS3HbbbfF1NjXBUBfpj5pIkqUTfoekqmkk79nzx7cdtttajO+ESNG4IQTTrBIST09SDIYEGt+8fxQKKQ0F4nahrAx41ESgJCgi1zaQZLMoftvknRBIpNy7XY7du7ciUWLFqnzL7zwQrz77ruHPBYZGRl4+umnccEFF6i+Yh/poYh33nkHt912m2UFeGlpKVwuF/r376/AJOZZ0j9jTRCakQsXLrRkjQDARRddhN///vdKcFHTkXTzkMdIUvPrczwecyZCB+OZiIYR3b7omDMRgfiooa5d5O9AdJdLMldKSgr69etn2aCNk11mRUiSSJ+EwXUtIZ/pcES3lG1vb1cghEzziSckJLjCe8h0KglfJ+ob+hVykaUknbnk3mD6O/BZTLYFoktBbr75Zvzxj3/EpEmTUF5ervxWaSqapqk2OX/ggQdQVVWFUCiEffv2AYjuIMNdb+jLypAI90f78MMPLcyVmpqKP/7xj5g8ebKK5UlUVWeoeH2lj+/BIIH6efH662Coz2mw0tJSpS106R1Pi3V0dGDixIkq7jNy5EhMmDBBTUZpfpFhup6pnq8zMycwzTdZgVb6UHJvYEpqiQAC3VvDcpKSOHHkBNLLVcv26OuuaK6tWLECO3bsiOnT8vJyOJ1OlJeXq4mqp2Dx3lVVVfD7/WhoaLAgpGPHjsWzzz6L4cOHW4AWKXAIfvzsZz/DH//4R0sbSkpKMGvWLNhs3VvAyklKIfXpp59aNrgAgK997Wt48sknVcFUjqFENxMFnOXxZJkrnuZKdN4xrcGSZXhOkra2Nss1DBBLE00yKf2qeJJJn9QS0ZNSUa4Ho6lI84331guW6ugc20qSpmk8yRxPipumGcNc2dnZmDhxIjIzM9XaLD6fzEmNa7NFN1vgytxAIACv14vFixcjHA5jw4YNuOqqq/DWW2+hpKQkLqpms0XXgskthICoJTF27FilHbnchf0hofpJkyZh6dKlaGtrU/329ttvo6amBldeeSWuueYatcpbH4tk5gnb+lVQn2MwoGdVLCdoJBLBz3/+c5XJ4Ha7MWrUKIRCIbWuSgZ1mUwqtYuUivI4/8t2cGLIoDH/U/vIGoE6gCKv0eNlnPxkXulzSVSRfRCJRCvqFhcXY//+/UhLS0NBQQEmT55sqdrL57ONTBbmZJUrs4Fovt/06dOxYsUKBAIBbN26FZdccgn+9re/YeTIkZZzTdNEfX09vvvd7+KDDz5Q7+hyuXDSSSchOztbaV7W2aA/ynGIRCLIysrCzJkz0djYiF27dqGqqgqmaWLFihUq+fimm26yhDMSka7VvmpApE8yGJCcJpPmGomTisyiI46Mu9B0lKtnSZLxdJNUxuPIQMypCwaDqp6idMj1GJ48xuOyvfIc+RvjVdQiaWlpmDVrFubNm4fBgwejpKQELpdLbSXE1QC8F6/ls1NSUiy1FDmBS0tLMX36dOzbtw9bt27Fli1bcMMNN+Cll15SMTYgGti//fbbLcw1ZswY5OXlqTibjOnRpJQMyhCKzWZDYWEh8vLyUFpail27dqGurg6maeL++++Hy+XCt7/97V7nBCmeL6/375dBfYrBdCRHrq3SgQ75m7xel2D0pfgnk0v1DRp000M+k76WPI+rgbkimos2pd+lM7A0HXXImYFZCcZI7UUTVNYYDIfDmD59OoDulc/U3NRURO1kSQT2E9OcyIA028rKylQC9MaNG7Fu3TpcfPHFePnllzFo0CA0NDTg9ttvx7x58wAAubm5KC8vx9ixY5WmlKutpUkIdGtouUqcVkZ5eTmKioqwfPlyNDU1we/34+6770Y4HMZ1111nAaM4xnIOxBvTeIzVE5rYGyV73rGVd6LR5s2bsX79evV94MCBMfCthHeZXe7xeBQAwonABFVds0hGiBck5THuBuJwOFQVKKAb+eP5lNjUJHoeIjegCwQCatm+LjTYZhaHkcs/pIbge/GZem1HaZ7yd17LNnIJDwDs2LEDTz/9NMLhMD7++GO8//77auJOmzZNBfal+SffGYhqPZ/PpxaHUsNJhJKrvk8++WRMnToVLpcLHR0duOeee/D888/HzAP5Ln2NjjkGk5O+oqLCsgNiv379LMFbHU2S6TupqakWX4XaRl8EyWvlxJExMWlCsvIStZosh6YTNbDNZlOlB3g/ABamYdoVtQ2XhZC4tIZZE3KxaCQSsTA776sHb/l+ss9cLhcAYOrU7ooPf//73/HKK6/gvvvuU+084YQTkJGRYdFW7G/WM5HvJf1TCkBZz4PtdjgcKCsrQ05OdLF8Z2cn3njjDcve2/FM6r6AjJOOOQaTaFq832TmgDTrJLTd3t5uKRjDCSdjWNI0o5klFwZyElBKOxwO7N27F6+88gpWrFihmIxAgtxdhPcFuoO3QLfwILPLojIsf8aSABJMkRkTNLOkCUWzmFWceJyTWJ/0kvncbjeysrLU7pGtra2YM2eOWsafm5uLYcOGKSaSpbN5H96XxP5g21lCgAJHaj7TNDFt2jS1fObzzz/H9773PVWhiv3Wl5hK0jHHYEA3k7EaE0nC6rKGobxOSnvpn+hQPScFNY1kWj5bxrC2b9+Ojz/+GJFIdH/oDz/8EBUVFfB6vSpJ2Ol0qvLTNMcYQ6MJRRCGNTXoR7W2tiIQCCA7OxvZ2dkx8TxOTuYCGoah3k2io7I2Iq+TsTa57zOfkZ+fjxkzZlhiUkCUUSZOnKiYmiAJmYmAD9OmpFUgwQdaAARD2FYKNCYP8F3feust7Nu3Ly5j9TVTsc8yWDyJJCU8AJxxxhk477zz1O8yZUhmHEgbXQcOKGGZwqMzmgzQ6hvekUmCwSCWLVtmKaZZX1+PlStX4v3330djYyN8Ph9aWlpUMR1WqyIjOZ1OVbGKlal8Ph+CwSC8Xi8WLFiAd955B2vXrlXak74Nq+sGAgH4/X4VmwO6gRhZm4NMJ4ENChAACkiR/pjb7UZpaallPE488US43W4lFCTqClhrp0hflu2XWpNBeonWEnm02+0YOnQoCgsL1ZjeeuutlnDK0dZgum+fLPUpFDFevEiSDpHrKKA0fXSSDCeRLD5L+jsSLSSTxQM/eGzTpk3qt7Fjx8Lj8aglJGS+3NxcjB8/Hl6vV5VpY7uY6c/2kwFqampQXV0Nv9+PxsZGANHlKK2trTjxxBOVKcV3IjQvGYzvJ3Pz+A7SfOZnTn7upMLaHzabDRMnTkQoFMLu3btRWFiInJwcixaXzCoFmRwjCiFpQsuqvhwDaU4TGR02bJiC7isqKvDOO+/gkksu+cpjXT1Rn2IwnaS/1RvzAdZJQ6icn3VfTL+HzEcEuhE9uUumDuNTA8j0otLSUvz+97/H3LlzsX79elRVVaG1tRWtra2oqqoCAIwePVpNKk5G3o9L8uvq6hKiY0wrovkozSwKA052KQx4L5n6RROYk5rv3tHRoYqiMiUMAE477TRkZ2djxIgRlvEgE1NgSE0lfTDZPmpJMhu1LU1zjh/fgSUZTNNEU1MTtm/frlDSvkp9msGAWAe2N2YLh8NwuVyIRCJqXZI0d3hOvOAy0A1z0+xhgU/6NAQBmKNIDQNEpf0VV1yB7OxsPPvss1i+fDk++eQT/O53v1P3BmApA304RKahdta3QWLGvVxsyf7TmUKCPbwPTU9qOPbB2LFjLXFIGdujL6ePjx4WkEgh/UL+JpFM3l8HSuS942nKvkJ9ygeT/lI8zRUvB+2OO+7A0KFDAUR9llWrVilJKJEzAh+UlBJelzY8TRxKYPouQPcSEUr7jo4OHDhwQC3XcDgcOOuss5TDP2PGDNxzzz349a9/rdp4MJSbm4sRI0bguuuus2zonijALrNKqCUk+ECm4kRnRjt9LLlhAxnF6/Va6owwOKw/m30JWLe3lf0s44xEV2VCtsz8oBCUY5OamopJkyapZz/++ONqW6e+Sn2OwfQ/kpS40jcqLi7GT3/6U3X9jh07sH//fgsYwuupfeTAS3ORAAAzv+kn6ROTE6qzsxN1dXXqGeeee27MZE5LS8OcOXOwePFi3Hnnnfj+979vMTvj0cyZM/GDH/wATz31FJYsWYIHH3zQguDJDBHmJHKHFMb/CBgw6E0fT8ae6F+xHiLPYxiCAIPUPmRc9gtDBvyT/hy1oI5cEqWUwiAeSMGMFDlesjQ5M2eO+2BJks5YiYKInCj8zxW0lKycKHa7XQVlJaqmpwfJwZEwMuNO6enpFp+BE6i5uVkxWFpaGq666iqFpukJwenp6fj5z38OIFotV8+V5GfDMNC/f38UFRVZzCO9jfSv5J8MJNO8k8f1Fd00HTs7O5W/JSe87Du+TyAQUMilBGqIwrK9NFF5H5nJQQSU2oqmKftBAlJysapc1ybnjKRE4MqRpIO5b59isGRImhykE044Ad/85jfx7LPPAgCWLFmCsrIyJWFpEjocDjXAegoVJxxNIU4qWQeeEyslJQX79u3DwoULAUQzHn7zm99gxowZMdkjkvh98uTJ6l2AWHQ0HsU7zglIM0sm1AJQe3HJfiMDSaSUQWwyFhlSbw/9WaacycC81PASdOE1zOKXwWfGAHmM10hEUo6F7CsSNdyhQOhfBvUpEzGZWAMHUkptm82Giy++WMVpQqEQtm7dasnU4ASUAywnAdANmcs4FdCdzcGBDgQC2LFjh2LGgoICXH755TGpRj3FZuL91hNzxZPUfFfGvrjFkdRkvKeEz6W5zXtz1bHL5YrRvrLNMq0rnk8rA/c0C3kNr5OZM2yjzPiQz5UhDRliOVbo2GptHKImGj16NJ5++mkMGDAAkUgEq1evxvr16xUA4Xa7AcAi5XWIG7CiXqZpWvb3om+ybt06VS13wIABeO655yzpSwdD8WB4aTayPaNGjVIal/mG3DWGwWqaU2wLTTy5w6YM8sqlItQoOsLKdkiNT4aib6WnfdEslKlk3Huto6ND7cCp+8AAVHoYU6jIsDrjyfb1Rc1FOuYYTAZVpX9gt9sxaNAgPPTQQ6qOxIYNG7Bt2zY1EZhJr/tz+oQm0zFtiMe9Xi82b96MrVu3wjRNlJaW4g9/+APGjh1ruTaRiahTMmk9vN/tt9+ukm/b29tRUVGhEMK0tDS43W7LCmaJ6JGJpBDhn4w9BYNBNZmlNpZoK7NP5CoAPd0snqaRZhxzI3mNFHDys4T76Q9L1PDcc89FYWFhj5bCV03JbCGbZhjGMsMw1hrRLWR/3nV8kGEYS43oVrEvG4aR2nXc2fV9R9fvA490o2kCSUYjKjZhwgQ89dRTylxct24dtm/frtYVBQIBZGZmwuPxWAAJTgx5P+nY19TUYNu2bVi3bh1M00RJSQlefPFFTJkyRfkR8agnjSZ9i2Tel5sxAFDvIhE65hLS5yHI097ebhEUMl5FgILoI1FJuRBTCoz8/HzLqgGgW9vTLJUaUY8v6snJbDeZkmvdeD7vzfuyqA4QTdXKzc39SjRYss9MRoMFAZxpmuYJACYAOM8wjJMAPIjoFrJDATQBuKHr/BsANHUd/13XeUmRDl7I4wlfQEhb/g0YMACPP/44hgwZgkgkghUrVuCjjz7Chg0b0NTUhMbGRjQ3NyMYDKqBJYMS3eKWsc3Nzdi8eTMWLlyItWvXIhKJYODAgXjqqacwatQolZHBtpCkqRnPvJFaRGe0eJrQ6XTi/vvvV+dwgzyaXVwhAFi3OqKPKaFwiSqy3RIdlbtsynQswzCwdetWbN682ZJlwT6UK64luEHmlevXWK+S7SUzkfnpM/Nd5GoHOS960pRHixLN03iUzOYPJgBv19eUrj8TwJkAvtV1/HkAPwPwR0S3kP1Z1/FXAfzBMAzDTEKPH4yq1xE43XkfP348/vCHP+D6669HTU0NgsEgNm7ciL1798IwDAwcOBCZmZmW9B5KcFaZraioQCQSsdR2z8/Px9NPP40xY8ZYVugeLZLMxmx8Zo54vV41wVlhWF+Sr/dpPMaXQWmagnICEU5fvHgxqqurYRgGRo0ahdGjR6sN/3ieBJ8AK7PrPpy0EnRkMTU1VRXMobZbvny5ateIESPwX//1XzHv9WVRss9LdgM+O4CVAIYCeAJABYBm0zS5HkRuE6u2kDVNM2QYRguAPACxOyb0/lz1X0pf3daXvgUHwzRNjB49Gn/7298wZ84cVY6ZzHIo6UpDhw7Fn//8Z4wbN85iIpGp49GhMKCEpKXQOPnkk3HllVeqcMSKFStUeTamHwHdKVnxtCPvL5lI17BSm9ntdrS3t2Pp0qWoqYluB2eaJjZt2oRgMIgTTzxRmZmyQA8Zl8+SC0xlPqhkeBn/oqYDokhiU1OTZR+zCy64wGIy91WwI6nRN00zbJrmBER3q5wKYOThPtgwjNmGYawwDGNFgt9jvif6k6tx+ce0nxEjRuBPf/oTHn74YcvG4wfRTmWe/eUvf8HEiRMt8HdPftThaDe+Fz8D0Yl27bXXqq1qOzo6sHHjRrS3tyuhogM4UpNIf1MGd8kAMqOfzw8Gg1iyZAlqampUX/CeO3fuVHuqkZnluMhKUrJyMZ8p/Uf6XjJDRWrZ/fv3q9qXqampuOmmm5SQjReA7it0UDPANM1mAJ8CmA4g2zAMakC5TazaQrbr9ywADXHu9ZRpmlNMUbAxGROxJ0aLd05qaipGjRqFa665Btu3b8fs2bNx9tlnq7VFiSgvLw9nnXUWrr32Wmzfvh233XYbxo0bZ0HP+Kx4dKjMpWsTfiezjR8/Hq+++ioGDx6MSCSC9evXY+PGjQqwkGarhOKlppDBZEL4EjKnuejz+bBw4UKlOc477zwsXrwYV111lZrcCxYsUAwuTU7C8ARQJIMzt1EGoqXJSGZlHuju3buxfPlyAFET/c9//jPy8/MtGv7LRhKTfV4yW8gWAOg0TbPZMAwXgHMQBS4+BXAZgJcAXAvgza5L3ur6vrjr90+S8b+0Z1peQH7X/+vmoyQJ6QPRWNgDDzwAwzDw6aefWnZ+1J9ZVlaGs88+24IqynMBKxMlYihK4UMhSnrdpBs+fDguv/xyPPjggzBNE1u2bMGoUaMQiUQUeBDPl5ILL40uuFyak2RQ+qF1dXUWs2zu3LnIy8vDHXfcgX/84x8IhUJoaGjAnj17MHToUEvBV+kH8v7sB2pX+ldkLpnHyD6PRCIKXAKi29uyWrCO/n6ZdCR9sBIAz3f5YTYAr5im+Y5hGJsAvGQYxi8ArEZ0H2d0/f+bYRg7ADQCuPJgGs7O6g2l0TWWfj07Xtr2crIyOz0eU8Zrk0TTAGvOYrzOlu2XvmS850iG1d9LZ2L+ftNNN2HdunV47733EIlEsGTJEowZMwaRSEQFdXXGZPwJQMwaKmq7cDiM1tZWtLW1YcWKqPWenp6Ou+++G8OHD0dqaipyc3PxyCOP4Kc//SlaW1uxfv16ZGVloaysDE6n0xLf4r1lv8gFrPIdZek2xuVWr16tTMOhQ4fiV7/6lcUklCimHBe9n+ONUbzfjjSz9qna9Js2bUJRUREAa35ePFhUn6B6Pp8EQXQIvKdO1++fCCjQzdLe4l3xAJt47yLbpSOl8vzOzk60trbilltusRT+HDlyJEaOHKkSoHm91Px8J8aXeA7TmRYvXqySmA3DwP33348bbrhBMSU13xtvvIE777wTwWAQTqcTM2bMQHFxsTID5XvRVJTmqv7O+lqwpUuXKnBq/PjxeOyxxzBmzBjlQ8brX+kH6n0bT8DFcy3ikS4ka2pqMGDAAN4nYW36PsVgn332GfLz8wHEMpgOOetOfKKOlJ3WUwcm0lzxfDwAMY61NFf0duj/k2WwROfQvAqHw6ivr8cdd9yBVatWqXZkZGRg7NixSE9PV5V+EwkAZt0Hg0Hs2bMH+/btQ2trq/r9pptuwi233GJhDBkaePXVV/Hgg9FQZ1pamiqZzfVlbKsO+/M9eU8WMAqFQmhra8OWLVuwceNGmKaJnJwcPPfcc6rupYT35X3i+eSJ5kVP/Z1IAMjPdXV1OPHEE3mo7zOY+PxVNuWYpERjmJ2djRNOOMHip+hCIRAIYMOGDaq+fzxKRtNLys3NxZQpU5CRkaFyCqkxabZK4AaAAkYqKyuxaNGiGK3d1+aF9t7HDoMdpyNLBQUFuOiii9DY2IiWlha1oNLj8cDj8eDjjz+O2fzuSFBOTg5mzpyJpqYmGIaB7OxstVm6ZB5q0FAohNbWVixdutSyod8xQn2ewQ4AaMchBKP/wykfx/skHvW1fhlgmmZBvB/6BIMBgGEYKxJJgf+rdLxP4tOx1C/H3HKV43ScjiU6zmDH6TgdRepLDPbUV92APkjH+yQ+HTP90md8sON0nP4TqS9psON0nP7j6CtnMMMwzjMMY6sRLTFwz1fdni+TDMN4xjCMOsMwNohjuYZhfGgYxvau/zldxw3DMB7r6qd1hmFMSnznY5cMwyg3DONTwzA2GdESFd/rOn5M9stXymBGNIH4CQDnAxgN4JuGYYz+Ktv0JdNzAM7Tjt0D4GPTNIcB+LjrOxDto2Fdf7MRXT3+n0ghAHeYpjkawEkA5nTNiWOyX75qDTYVwA7TNHeaptmB6NKXS77iNn1pZJrmfERXHEi6BNESDOj6f6k4/oIZpSWIrscr+VIa+iWSaZo1pmmu6vrcBmAzoqvkj8l++aoZTJUX6CJZeuD/KhWZplnT9Xk/gKKuz//n+sqIViSbCGApjtF++aoZ7Dj1QF0LVf9PwryGYXgAvAbg+6ZptsrfjqV++aoZTJUX6CJZeuD/KtXSxOn6z+1b/s/0lWEYKYgy199N0/xX1+Fjsl++agZbDmCYES1imoro6ue3vuI2fdXEkgtAbCmGa7pQs5MAtAiT6T+GjOi6lL8C2Gya5qPip2OzX/QFjV/2H4ALAGxDtBTcj77q9nzJ7/4PADUAOhH1HW5AtMTdxwC2A/gIQG7XuQa6S+atBzDlq27/UeqTGYiaf+sArOn6u+BY7ZfjmRzH6TgdRfqqTcTjdJz+o+k4gx2n43QU6TiDHafjdBTpOIMdp+N0FOk4gx2n43QU6TiDHafjdBTpOIMdp+N0FOk4gx2n43QU6f8H5ExMJJG/TLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import ndimage\n",
    "from skimage.feature import peak_local_max\n",
    "from skimage.segmentation import watershed\n",
    "# Load the coins image\n",
    "img = cv2.imread(\"/home/santanu/Downloads/coins.jpg\")\n",
    "# Convert the image to gray scale\n",
    "imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "plt.figure(1)\n",
    "plt.imshow(imgray,cmap='gray')\n",
    "# Threshold the image to convert it to Binary image based on Otsu's method\n",
    "thresh = cv2.threshold(imgray, 0, 255,\n",
    "cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n",
    "\n",
    "\"\"\"\n",
    " Detect the contours and display them\n",
    " As we can see in the 2nd image below that the contours are not prominent at the regions of\n",
    " overlap with normal thresholding method. However with Wateshed algorithm the \n",
    " the same is possible because of its ability to better separate regions of overlap by\n",
    " building watersheds at the boundaries of different basins of pixel intensity minima\n",
    "\"\"\"\n",
    "contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
    "y = cv2.drawContours(imgray, contours, -1, (0,255,0), 3)\n",
    "plt.figure(2)\n",
    "plt.imshow(y,cmap='gray')\n",
    "\"\"\"\n",
    " Hence we will proceed with the Watershed algorithm so that each of the coin form its own\n",
    " cluster and hence its possible to have separate contours for each coin.\n",
    " Relabel the thresholded image to be consisting of only 0 and 1\n",
    " as the input image to distance_transform_edt should be in this format.\n",
    "\"\"\"\n",
    "thresh[thresh == 255] = 5\n",
    "thresh[thresh == 0] = 1\n",
    "thresh[thresh == 5] = 0\n",
    "\"\"\"\n",
    " The distance_transform_edt and the peak_local_max functions helps building the markers by detecting\n",
    " points near the centre points of the coins. One can skip these steps and create a marker\n",
    " manually by setting one pixel within each coin with a random number represneting its cluster\n",
    "\"\"\"\n",
    "D = ndimage.distance_transform_edt(thresh)\n",
    "localMax = peak_local_max(D, indices=False, min_distance=10,\n",
    "labels=thresh)\n",
    "markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]\n",
    "\"\"\"\n",
    " Provide the EDT distance matrix and the markers to the watershed algorithm to detect the clusters\n",
    " labels for each pixel. For each coin, the pixels corresponding to it will be filled with the cluster number\n",
    "\"\"\"\n",
    "labels = watershed(-D, markers, mask=thresh)\n",
    "print(\"[INFO] {} unique segments found\".format(len(np.unique(labels)) - 1))\n",
    "#Create the contours for each label(each coin and append to the plot)\n",
    "for k in np.unique(labels):\n",
    "    if k != 0 :\n",
    "        labels_new = labels.copy()\n",
    "        labels_new[labels == k] = 255\n",
    "        labels_new[labels != k] = 0\n",
    "        labels_new = np.array(labels_new,dtype='uint8')\n",
    "        contours, hierarchy = cv2.findContours(labels_new,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
    "        z = cv2.drawContours(imgray,contours, -1, (0,255,0), 3)\n",
    "        plt.figure(3)\n",
    "        plt.imshow(z,cmap='gray') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9039a4db-0262-48b9-a47d-d47759131152",
   "metadata": {},
   "source": [
    "# Listing 6-3.Image Segmentation using K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a7cd4f8-d81a-4b12-86c2-c3a9f80f445a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8d8dcf3fa0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(0)\n",
    "\"\"\"\n",
    "  K means that one has used in Machine learning \n",
    " clustering also provides good segmentation as we see below\n",
    "\"\"\"\n",
    "\n",
    "img = cv2.imread(\"/home/santanu/Downloads/kmeans1.jfif\")\n",
    "imgray_ori = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "plt.figure(1)\n",
    "plt.imshow(imgray_ori,cmap='gray')\n",
    "# Save the dimensions of the image\n",
    "row,col,depth = img.shape\n",
    "# Collapse the row and column axis for faster matrix operation.\n",
    "img_new = np.zeros(shape=(row*col,3))\n",
    "glob_ind = 0 \n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        u = np.array([img[i,j,0],img[i,j,1],img[i,j,2]]) \n",
    "        img_new[glob_ind,:] = u\n",
    "        glob_ind += 1\n",
    "\n",
    "\"\"\"\n",
    " Set the number of clusters\n",
    " One can experiment with different values of K and select \n",
    " the one that provides good clustering. Having said that Image processing\n",
    " especially image enhancement and segmentation to some extent is subjective.\n",
    "\"\"\"\n",
    "K = 5\n",
    "num_iter = 20\n",
    "\"\"\"\n",
    " K means suffers from local minima solution and hence \n",
    " its better to trigger K-means several times with different random seed value\n",
    "\"\"\"\n",
    "for g in range(num_iter):\n",
    "    # Define cluster for storing the cluster number and out_dist to store the distances from centroid\n",
    "    clusters = np.zeros((row*col,1))\n",
    "    out_dist = np.zeros((row*col,K))\n",
    "    centroids = np.random.randint(0,255,size=(K,3))\n",
    "\n",
    "    for k in range(K):\n",
    "        diff = img_new - centroids[k,:]\n",
    "        diff_dist = np.linalg.norm(diff,axis=1)\n",
    "        out_dist[:,k] = diff_dist\n",
    "\n",
    "# Assign the cluster with minimum distance to a pixel location\n",
    "\n",
    "    clusters = np.argmin(out_dist,axis=1)\n",
    "\n",
    "# Recompute the clusters\n",
    "\n",
    "    for k1 in np.unique(clusters):\n",
    "        centroids[k1,:] = np.sum(img_new[clusters == k1,:],axis=0)/np.sum([clusters == k1])\n",
    "\n",
    "# Reshape the cluster labels in two dimensional image form\n",
    "clusters = np.reshape(clusters,(row,col))\n",
    "out_image = np.zeros(img.shape)\n",
    "\n",
    "#Form the 3-D image with the labels replaced by their correponding centroid pixel intensity\n",
    "\n",
    "for i in range(row):\n",
    "    for j in range(col):\n",
    "        out_image[i,j,0] = centroids[clusters[i,j],0]\n",
    "        out_image[i,j,1] = centroids[clusters[i,j],1]\n",
    "        out_image[i,j,2] = centroids[clusters[i,j],2]\n",
    "        out_image = np.array(out_image,dtype=\"uint8\")\n",
    "\n",
    "# Display the output image after converting into gray scale\n",
    "# Readers adviced to display the image as it is for better clarity\n",
    "imgray = cv2.cvtColor(out_image,cv2.COLOR_BGR2GRAY)\n",
    "plt.figure(2)\n",
    "plt.imshow(imgray,cmap='gray') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94dcfd3d-b8ba-44ad-8891-a3bbd5427789",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, Model\n",
    "layers.MaxPool2D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c783f5d5-c017-46db-9bcd-c83fe122d152",
   "metadata": {},
   "source": [
    "# Listing 6-4.Semantic Segmentation in TensorFlow with Fully Connected Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "85789f0f-07b9-4ac7-8427-e1369d83c1dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.9.1\n"
     ]
    }
   ],
   "source": [
    "#-----------------------------------------------\n",
    "# Load the different packages \n",
    "#-----------------------------------------------\n",
    "import tensorflow as tf\n",
    "print(f\"Tensorflow version: {tf.__version__}\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "from subprocess import check_output\n",
    "import numpy as np\n",
    "from tensorflow.keras.utils import img_to_array, array_to_img, load_img\n",
    "from  skimage.transform import resize\n",
    "from tensorflow.keras import layers, Model\n",
    "from pathlib import Path\n",
    "import imageio\n",
    "\n",
    "class segmentation_model(Model):\n",
    "    def __init__(self):\n",
    "        super(segmentation_model,self).__init__()\n",
    "        self.conv11, self.conv12, self.pool1  = self.conv_block(filters=64,kernel_size=3,strides=1,\n",
    "                                                                padding='SAME',activation='relu',\n",
    "                                                                pool_size=2,pool_stride=2)\n",
    "        \n",
    "        self.conv21, self.conv22, self.pool2  = self.conv_block(filters=128,kernel_size=3,strides=1,\n",
    "                                                                padding='SAME',activation='relu',\n",
    "                                                                pool_size=2,pool_stride=2)\n",
    "        \n",
    "        self.conv31, self.conv32, self.pool3  = self.conv_block(filters=256,kernel_size=3,strides=1,\n",
    "                                                                padding='SAME',activation='relu',\n",
    "                                                                pool_size=2,pool_stride=2)\n",
    "        \n",
    "        self.conv41, self.conv42, self.pool4  = self.conv_block(filters=512,kernel_size=3,strides=1,\n",
    "                                                                padding='SAME',activation='relu',\n",
    "                                                                pool_size=2,pool_stride=2)\n",
    "        \n",
    "        self.conv51, self.conv52              = self.conv_block(filters=1024,kernel_size=3,strides=1,\n",
    "                                                                padding='SAME',activation='relu',\n",
    "                                                                pool_size=2,pool_stride=2,pool=False)\n",
    "        \n",
    "        self.deconv1 = self.deconv_block(filters=1024,kernel_size=3,strides=2,padding='SAME',activation='relu')\n",
    "        self.deconv2 = self.deconv_block(filters=512,kernel_size=3,strides=2,padding='SAME',activation='relu')\n",
    "        self.deconv3 = self.deconv_block(filters=256,kernel_size=3,strides=2,padding='SAME',activation='relu')\n",
    "        self.deconv4 = self.deconv_block(filters=128,kernel_size=3,strides=2,padding='SAME',activation='relu')\n",
    "        #self.deconv5 = self.deconv_block(filters=64,kernel_size=3,strides=2,padding='SAME',activation='relu')\n",
    "        \n",
    "        self.convf = layers.Conv2D(filters=1,kernel_size=1,strides=1,padding='SAME')\n",
    "        \n",
    "\n",
    "        \n",
    "    def conv_block(self,filters,kernel_size,strides,padding,activation,pool_size,pool_stride,pool=True):\n",
    "        conv11 = layers.Conv2D(filters=filters,kernel_size=kernel_size,strides=(strides,strides),padding=padding,activation=activation)\n",
    "        conv12 = layers.Conv2D(filters=filters,kernel_size=kernel_size,strides=(strides,strides),padding=padding,activation=activation)\n",
    "        if pool:\n",
    "            pool1  = layers.MaxPool2D(pool_size=(pool_size,pool_size),strides=(pool_stride,pool_stride))\n",
    "            return conv11, conv12, pool1\n",
    "        return conv11, conv12\n",
    "                                         \n",
    "        \n",
    "    def deconv_block(self,filters,kernel_size,strides,padding,activation):\n",
    "        deconv1 = layers.Conv2DTranspose(filters=filters,kernel_size=kernel_size,strides=(strides,strides),padding=padding,activation=activation)\n",
    "        return deconv1\n",
    "                                         \n",
    "        \n",
    "        \n",
    "    \n",
    "    def call(self,x):\n",
    "        x = self.conv11(x)\n",
    "        x = self.conv12(x)\n",
    "        x = self.pool1(x)\n",
    "        #print(x.shape)\n",
    "        #\n",
    "        x = self.conv21(x)\n",
    "        x = self.conv22(x)\n",
    "        x = self.pool2(x)\n",
    "        #print(x.shape)\n",
    "        #\n",
    "        x = self.conv31(x)\n",
    "        x = self.conv32(x)\n",
    "        x = self.pool3(x)\n",
    "        #print(x.shape)\n",
    "        #\n",
    "        x = self.conv41(x)\n",
    "        x = self.conv42(x)\n",
    "        x = self.pool4(x)\n",
    "        #print(x.shape)\n",
    "        #\n",
    "        x = self.conv51(x)\n",
    "        x = self.conv52(x)\n",
    "        #x = self.pool5(x)\n",
    "        print(x.shape)\n",
    "        #\n",
    "        x = self.deconv1(x)\n",
    "        #print('d1',x.shape)\n",
    "        x = self.deconv2(x)\n",
    "        #print('d2',x.shape)\n",
    "        x = self.deconv3(x)\n",
    "        #print('d3',x.shape)\n",
    "        x = self.deconv4(x)\n",
    "        #print('d4',x.shape)\n",
    "        #print(x.shape)\n",
    "        #\n",
    "        #x = self.deconv5(x)\n",
    "        print(x.shape)\n",
    "        x = self.convf(x)\n",
    "        \n",
    "        return x \n",
    "        \n",
    "        \n",
    "  \n",
    "\n",
    "def grey2rgb(img):\n",
    "    \"\"\"\n",
    "     utility function to convert greyscale images to rgb\n",
    "    \"\"\"\n",
    "    new_img = []\n",
    "    for i in range(img.shape[0]):\n",
    "        for j in range(img.shape[1]):\n",
    "            new_img.append(list(img[i][j])*3)\n",
    "    new_img = np.array(new_img).reshape(img.shape[0], img.shape[1], 3)\n",
    "    return new_img\n",
    "\n",
    "\n",
    "def data_gen_small(data_dir, mask_dir, images, batch_size, dims):\n",
    "    \"\"\"\n",
    "    Generator that we will use to read the data from the directory\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        ix = np.random.choice(np.arange(len(images)), batch_size)\n",
    "        imgs = []\n",
    "        labels = []\n",
    "        for i in ix:\n",
    "            # images\n",
    "            img_path = f\"{Path(data_dir)}/{images[i]}\"\n",
    "            original_img = imageio.imread(img_path)\n",
    "            \n",
    "            resized_img = resize(original_img, dims)\n",
    "            array_img = img_to_array(resized_img)\n",
    "            imgs.append(array_img)\n",
    "                \n",
    "            # masks\n",
    "            prefix = images[i].split(\".\")[0]\n",
    "            mask_path = f\"{Path(mask_dir)}/{prefix}_mask.gif\"\n",
    "            original_mask = imageio.imread(mask_path)\n",
    "            resized_mask = resize(original_mask, dims)\n",
    "            array_mask = img_to_array(resized_mask)\n",
    "            labels.append(array_mask[:, :, 0])\n",
    "        imgs = np.array(imgs)\n",
    "        labels = np.array(labels)\n",
    "        yield imgs, labels.reshape(-1, dims[0], dims[1],1)\n",
    "            \n",
    "def train(data_dir,mask_dir,batch_size=4,train_val_split=[0.8,0.2],img_height=128,img_width=128,lr=0.01,num_batches=500):\n",
    "    \n",
    "    model = segmentation_model()\n",
    "    model_graph = tf.function(model)\n",
    "    \n",
    "    # Get the path to all images for dynamic fetch in each batch generation\n",
    "    all_images = os.listdir(data_dir)\n",
    "    # Train val split  \n",
    "    train_images, validation_images = train_test_split(all_images, train_size=train_val_split[0], test_size=train_val_split[1])\n",
    "    # Create train and val generator\n",
    "    train_gen = data_gen_small(data_dir, mask_dir, train_images,batch_size=batch_size,dims=(img_height,img_width))\n",
    "    validation_gen = data_gen_small(data_dir, mask_dir, validation_images,batch_size=batch_size, dims=(img_height,img_width))\n",
    "    \n",
    "    optimizer = tf.keras.optimizers.Adam(lr)\n",
    "    loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "    \n",
    "\n",
    "\n",
    "    for batch in range(num_batches):\n",
    "        \n",
    "        X_batch,y_batch = next(train_gen)\n",
    "        X_batch, y_batch =  tf.constant(X_batch), tf.constant(y_batch)\n",
    "        \n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred_batch = model_graph(X_batch)\n",
    "            loss_ = loss_fn(y_batch,y_pred_batch)\n",
    "        \n",
    "        # Compute gradient\n",
    "        gradients = tape.gradient(loss_, model.trainable_variables)\n",
    "        # Update the parameters\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "        \n",
    "        X_val,y_val = next(validation_gen)\n",
    "        X_val,y_val = tf.constant(X_val), tf.constant(y_val)\n",
    "        y_val_pred = model_graph(X_val,training=False)\n",
    "        loss_val = loss_fn(y_val,y_val_pred)\n",
    "        print(f\"Batch : {batch} , train loss:{loss_.numpy()/batch_size}, val loss: {loss_val/batch_size}\")\n",
    "    return model, model_graph, X_val,y_val, y_val_pred\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0290f468-4a0e-4704-b767-1ff383cf4819",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "height,width=128,128\n",
    "data_dir = \"/media/santanu/9eb9b6dc-b380-486e-b4fd-c424a325b976/Kaggle Competitions/Carvana/train/\"\n",
    "mask_dir = \"/media/santanu/9eb9b6dc-b380-486e-b4fd-c424a325b976/Kaggle Competitions/Carvana/train_masks/\"\n",
    "model, model_graph, X_val,y_val, y_val_pred = train(data_dir,mask_dir,batch_size=4,train_val_split=[0.8,0.2],img_height=height,img_width=width,lr=0.0002,num_batches=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9364a832-df05-4103-be2b-7458f4e6b790",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cb87dd940>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Validating results \n",
    "     1st image - Output segmented image\n",
    "     2nd image - Original image\n",
    "     3rd image - Ground truth\n",
    "\"\"\"\n",
    "img = (y_val_pred.numpy()[2] > 0.5)*1.0\n",
    "plt.figure(1)\n",
    "plt.imshow(grey2rgb(img),alpha=0.5)\n",
    "plt.figure(2)\n",
    "plt.imshow(X_val.numpy()[1])\n",
    "plt.figure(3)\n",
    "plt.imshow(grey2rgb(y_val.numpy()[1]), alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "ad0faf8d-d7bc-40bf-b8be-2be7a163e2f6",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cb1b0fb20>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Validating results \n",
    "     1st image - Output segmented image\n",
    "     2nd image - Original image\n",
    "     3rd image - Ground truth\n",
    "\"\"\"\n",
    "img = (y_val_pred.numpy()[2] > 0.5)*1.0\n",
    "plt.figure(4)\n",
    "plt.imshow(grey2rgb(img),alpha=0.5)\n",
    "plt.figure(5)\n",
    "plt.imshow(X_val.numpy()[2])\n",
    "plt.figure(6)\n",
    "plt.imshow(grey2rgb(y_val.numpy()[2]), alpha=0.5) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aa44b77",
   "metadata": {},
   "source": [
    "# Listing 6-5. Implementation of a Generative Adversarial Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "bdc6a7b2-5584-4acd-96cd-57c6c5883a15",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Iteration : 0, Discrinator loss: 1.1017875671386719, Generator loss: 0.7137295007705688\n",
      "Epoch: 0 Iteration : 200, Discrinator loss: 0.2998032569885254, Generator loss: 1.2036678791046143\n",
      "Epoch: 1 Iteration : 0, Discrinator loss: 0.2501879930496216, Generator loss: 1.4736047983169556\n",
      "Epoch: 1 Iteration : 200, Discrinator loss: 0.1949506402015686, Generator loss: 1.8274062871932983\n",
      "Epoch: 2 Iteration : 0, Discrinator loss: 0.2268180251121521, Generator loss: 2.093841314315796\n",
      "Epoch: 2 Iteration : 200, Discrinator loss: 0.32480332255363464, Generator loss: 1.6181516647338867\n",
      "Epoch: 3 Iteration : 0, Discrinator loss: 0.3639432489871979, Generator loss: 1.5107319355010986\n",
      "Epoch: 3 Iteration : 200, Discrinator loss: 0.42706114053726196, Generator loss: 1.185171127319336\n",
      "Epoch: 4 Iteration : 0, Discrinator loss: 0.28556936979293823, Generator loss: 1.5028413534164429\n",
      "Epoch: 4 Iteration : 200, Discrinator loss: 0.3450779914855957, Generator loss: 1.3449459075927734\n",
      "Epoch: 5 Iteration : 0, Discrinator loss: 0.2964794635772705, Generator loss: 1.2333242893218994\n",
      "Epoch: 5 Iteration : 200, Discrinator loss: 0.5263453125953674, Generator loss: 1.1822922229766846\n",
      "Epoch: 6 Iteration : 0, Discrinator loss: 0.47319352626800537, Generator loss: 1.3950377702713013\n",
      "Epoch: 6 Iteration : 200, Discrinator loss: 0.5344085693359375, Generator loss: 1.1851046085357666\n",
      "Epoch: 7 Iteration : 0, Discrinator loss: 0.5629163980484009, Generator loss: 0.9279212951660156\n",
      "Epoch: 7 Iteration : 200, Discrinator loss: 0.41798466444015503, Generator loss: 1.483992338180542\n",
      "Epoch: 8 Iteration : 0, Discrinator loss: 0.5574295520782471, Generator loss: 0.881244421005249\n",
      "Epoch: 8 Iteration : 200, Discrinator loss: 0.34019559621810913, Generator loss: 1.4944649934768677\n",
      "Epoch: 9 Iteration : 0, Discrinator loss: 0.33809423446655273, Generator loss: 1.4336414337158203\n",
      "Epoch: 9 Iteration : 200, Discrinator loss: 0.4700615406036377, Generator loss: 1.3585829734802246\n",
      "Epoch: 10 Iteration : 0, Discrinator loss: 0.6268720626831055, Generator loss: 1.1567668914794922\n",
      "Epoch: 10 Iteration : 200, Discrinator loss: 0.47605058550834656, Generator loss: 1.301950216293335\n",
      "Epoch: 11 Iteration : 0, Discrinator loss: 0.4365772604942322, Generator loss: 1.2711844444274902\n",
      "Epoch: 11 Iteration : 200, Discrinator loss: 0.6501534581184387, Generator loss: 1.4859219789505005\n",
      "Epoch: 12 Iteration : 0, Discrinator loss: 0.5841761827468872, Generator loss: 1.3300789594650269\n",
      "Epoch: 12 Iteration : 200, Discrinator loss: 0.43344083428382874, Generator loss: 2.36750864982605\n",
      "Epoch: 13 Iteration : 0, Discrinator loss: 0.6400217413902283, Generator loss: 1.0622859001159668\n",
      "Epoch: 13 Iteration : 200, Discrinator loss: 0.5447341203689575, Generator loss: 0.9190360307693481\n",
      "Epoch: 14 Iteration : 0, Discrinator loss: 0.6225481033325195, Generator loss: 0.8385011553764343\n",
      "Epoch: 14 Iteration : 200, Discrinator loss: 0.42368441820144653, Generator loss: 1.2204477787017822\n",
      "Epoch: 15 Iteration : 0, Discrinator loss: 0.5015592575073242, Generator loss: 1.1660429239273071\n",
      "Epoch: 15 Iteration : 200, Discrinator loss: 0.6492427587509155, Generator loss: 0.7618398666381836\n",
      "Epoch: 16 Iteration : 0, Discrinator loss: 0.4622490108013153, Generator loss: 1.1729133129119873\n",
      "Epoch: 16 Iteration : 200, Discrinator loss: 0.6131242513656616, Generator loss: 0.9043179750442505\n",
      "Epoch: 17 Iteration : 0, Discrinator loss: 0.4759373664855957, Generator loss: 1.598825454711914\n",
      "Epoch: 17 Iteration : 200, Discrinator loss: 0.5254887342453003, Generator loss: 0.994986891746521\n",
      "Epoch: 18 Iteration : 0, Discrinator loss: 0.5440883636474609, Generator loss: 1.2683334350585938\n",
      "Epoch: 18 Iteration : 200, Discrinator loss: 0.46908706426620483, Generator loss: 1.444332480430603\n",
      "Epoch: 19 Iteration : 0, Discrinator loss: 0.6213617324829102, Generator loss: 0.8111793994903564\n",
      "Epoch: 19 Iteration : 200, Discrinator loss: 0.6093022227287292, Generator loss: 0.9208083152770996\n",
      "Epoch: 20 Iteration : 0, Discrinator loss: 0.42103439569473267, Generator loss: 1.456479787826538\n",
      "Epoch: 20 Iteration : 200, Discrinator loss: 0.4698140025138855, Generator loss: 1.2846643924713135\n",
      "Epoch: 21 Iteration : 0, Discrinator loss: 0.6000527143478394, Generator loss: 1.0108025074005127\n",
      "Epoch: 21 Iteration : 200, Discrinator loss: 0.3864519000053406, Generator loss: 1.5159918069839478\n",
      "Epoch: 22 Iteration : 0, Discrinator loss: 0.4827062487602234, Generator loss: 1.3563354015350342\n",
      "Epoch: 22 Iteration : 200, Discrinator loss: 0.4698881506919861, Generator loss: 1.3355371952056885\n",
      "Epoch: 23 Iteration : 0, Discrinator loss: 0.46509408950805664, Generator loss: 1.350873589515686\n",
      "Epoch: 23 Iteration : 200, Discrinator loss: 0.48266011476516724, Generator loss: 1.3878684043884277\n",
      "Epoch: 24 Iteration : 0, Discrinator loss: 0.4926946759223938, Generator loss: 1.0238192081451416\n",
      "Epoch: 24 Iteration : 200, Discrinator loss: 0.45097270607948303, Generator loss: 1.4874517917633057\n",
      "Epoch: 25 Iteration : 0, Discrinator loss: 0.3884570300579071, Generator loss: 1.470395565032959\n",
      "Epoch: 25 Iteration : 200, Discrinator loss: 0.4861106276512146, Generator loss: 1.494809627532959\n",
      "Epoch: 26 Iteration : 0, Discrinator loss: 0.4910321831703186, Generator loss: 1.5057940483093262\n",
      "Epoch: 26 Iteration : 200, Discrinator loss: 0.6068161129951477, Generator loss: 1.0563955307006836\n",
      "Epoch: 27 Iteration : 0, Discrinator loss: 0.49871131777763367, Generator loss: 1.230818748474121\n",
      "Epoch: 27 Iteration : 200, Discrinator loss: 0.44278526306152344, Generator loss: 1.6105163097381592\n",
      "Epoch: 28 Iteration : 0, Discrinator loss: 0.5630109906196594, Generator loss: 0.9127862453460693\n",
      "Epoch: 28 Iteration : 200, Discrinator loss: 0.525017499923706, Generator loss: 1.0204137563705444\n",
      "Epoch: 29 Iteration : 0, Discrinator loss: 0.5401397943496704, Generator loss: 1.1612029075622559\n",
      "Epoch: 29 Iteration : 200, Discrinator loss: 0.395643025636673, Generator loss: 1.9329397678375244\n",
      "Epoch: 30 Iteration : 0, Discrinator loss: 0.5126255750656128, Generator loss: 1.1977403163909912\n",
      "Epoch: 30 Iteration : 200, Discrinator loss: 0.5447165966033936, Generator loss: 1.0024569034576416\n",
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      "(256, 784)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, Model, initializers,activations\n",
    "\n",
    "## The dimension of the Prior Noise Signal is 100 \n",
    "## The generator would have 150 and 300 hidden units successively before 784 outputs corresponding\n",
    "## to 28x28 image size\n",
    "\n",
    "h1_dim = 150\n",
    "h2_dim = 300\n",
    "dim = 100\n",
    "batch_size = 256\n",
    "\n",
    "class generator(Model):\n",
    "    def __init__(self,hidden_units=[500,500]):\n",
    "        super(generator,self).__init__()\n",
    "        self.fc1 = layers.Dense(hidden_units[0],activation='relu',kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.1))\n",
    "        self.fc2 = layers.Dense(hidden_units[1],activation='relu',kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.1))\n",
    "        self.fc3 = layers.Dense(28*28,activation=activations.tanh,kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.1))\n",
    "    \n",
    "    def call(self,x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    \n",
    "class discriminator(Model):\n",
    "    def __init__(self,hidden_units=[500,500],dropout_rate=0.3):\n",
    "        super(discriminator,self).__init__()\n",
    "        self.fc1 = layers.Dense(hidden_units[0],activation='relu',kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.1))\n",
    "        self.drop1 = layers.Dropout(rate=dropout_rate)\n",
    "        self.fc2 = layers.Dense(hidden_units[1],activation='relu',kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.1))\n",
    "        self.drop2 = layers.Dropout(rate=dropout_rate)\n",
    "        self.fc3 = layers.Dense(1,kernel_initializer=initializers.TruncatedNormal(mean=0.0, stddev=0.1))\n",
    "        \n",
    "    \n",
    "    def call(self,x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.drop1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.drop2(x)\n",
    "        x = self.fc3(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "class GAN(Model):\n",
    "    def __init__(self,G,D):\n",
    "        super(GAN,self).__init__()\n",
    "        self.G = G\n",
    "        self.D = D\n",
    "        \n",
    "    def call(self,z):\n",
    "        z = self.G(z)\n",
    "        z = self.D(z)\n",
    "        return z\n",
    "\n",
    "\n",
    "        \n",
    "    \n",
    "        \n",
    "        \n",
    "\n",
    "        \n",
    "def data_load():\n",
    "    (train_X, train_Y), (test_X, test_Y) = tf.keras.datasets.mnist.load_data()\n",
    "    train_X, test_X , = train_X.reshape(-1,28*28), test_X.reshape(-1,28*28)\n",
    "    train_X, test_X = train_X/255.0, test_X/255.0\n",
    "    train_X, test_X = 2*train_X - 1, 2*test_X - 1\n",
    "    return np.float32(train_X), train_Y, np.float32(test_X), test_Y\n",
    "    \n",
    "def train(lr=0.0001,batch_size=256,hidden_units=[150,130],dim=100,dropout_rate=0.3,num_epochs=300):\n",
    "    \n",
    "    G_ = generator(hidden_units=hidden_units)\n",
    "    D_ = discriminator(hidden_units=hidden_units[::-1],dropout_rate=dropout_rate)\n",
    "    model = GAN(G_,D_)\n",
    "    G_graph, D_graph, model_graph = tf.function(G_), tf.function(D_), tf.function(model)    \n",
    "    \n",
    "    optimizer = tf.keras.optimizers.Adam(lr)\n",
    "    \n",
    "    train_X, train_Y, test_X, test_Y = data_load()\n",
    "    num_train = train_X.shape[0]\n",
    "    order = np.arange(num_train)\n",
    "    num_batches = num_train//batch_size\n",
    "    \n",
    "    loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.NONE)\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        np.random.shuffle(order)\n",
    "        train_X, train_Y = train_X[order], train_Y[order]\n",
    "        \n",
    "        for i in range(num_batches):\n",
    "            x_ = train_X[batch_size*i:(i+1)*batch_size]\n",
    "            z_ = np.random.uniform(-1, 1, size=(x_.shape[0],dim)).astype(np.float32)\n",
    "            y_label_real_dis = np.array([1. for i in range(x_.shape[0])]).reshape(-1,1)\n",
    "            y_label_fake_dis = np.array([0. for i in range(x_.shape[0])]).reshape(-1,1)\n",
    "            y_label_gen = np.array([1. for i in range(x_.shape[0])]).reshape(-1,1)\n",
    "            \n",
    "            x_,z_, y_label_real_dis, y_label_fake_dis,y_label_gen = tf.constant(x_), tf.constant(z_), tf.constant(y_label_real_dis) , tf.constant(y_label_fake_dis), tf.constant(y_label_gen)\n",
    "            \n",
    "\n",
    "            with tf.GradientTape(persistent=True) as tape:\n",
    "                y_pred_fake = model_graph(z_,training=True)\n",
    "                y_pred_real = D_graph(x_,training=True)\n",
    "                \n",
    "                #print(loss_fn(y_label_fake_dis,y_pred_fake))\n",
    "               \n",
    "                loss_discrimator = 0.5*tf.reduce_mean(loss_fn(y_label_fake_dis,y_pred_fake) +  loss_fn(y_label_real_dis,y_pred_real))\n",
    "                loss_generator = tf.reduce_mean(loss_fn(y_label_gen,y_pred_fake))\n",
    "            # Compute gradient\n",
    "            grad_d = tape.gradient(loss_discrimator, D_.trainable_variables)\n",
    "            grad_g = tape.gradient(loss_generator, G_.trainable_variables)\n",
    "            # update the parameters\n",
    "            optimizer.apply_gradients(zip(grad_d, D_.trainable_variables))\n",
    "            optimizer.apply_gradients(zip(grad_g, G_.trainable_variables))\n",
    "            del tape\n",
    "            if (i % 200) == 0:   \n",
    "                print (f\"Epoch: {epoch} Iteration : {i}, Discrinator loss: {loss_discrimator.numpy()}, Generator loss: {loss_generator.numpy()}\")\n",
    "    \n",
    "    # Generator some images \n",
    "    z_ = tf.constant(np.random.uniform(-1, 1, size=(batch_size,dim)).astype(np.float32))\n",
    "    imgs = 0.5*(G_graph(z_,training=False) + 1).numpy()\n",
    "    print(imgs.shape)\n",
    "    for k in range(36):\n",
    "        plt.subplot(6,6,k+1)\n",
    "        image = np.reshape(imgs[k],(28,28))\n",
    "        plt.imshow(image,cmap='gray')\n",
    "        \n",
    "    return G_, D_, model, G_graph, D_graph, model_graph, imgs    \n",
    "    \n",
    "\n",
    "G_, D_, model, G_graph, D_graph, model_graph,imgs_val   = train()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38fd972",
   "metadata": {},
   "source": [
    "# Listing 6-6. Implementation of a CycleGAN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "602ae206-4224-47aa-bba4-f01c7f96e4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "# import scipy\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, Model\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "# import sys\n",
    "# from data_loader import DataLoader\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import glob\n",
    "import copy\n",
    "from imageio import imread, imsave\n",
    "#from skimage.transform import resize \n",
    "from cv2 import resize\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "\n",
    "def load_train_data(image_path, dim=64, is_testing=False):\n",
    "    img_A = imread(image_path[0])\n",
    "    img_B = imread(image_path[1])\n",
    "    # Resize\n",
    "    #print('1',img_A.max(),img_A.min()) \n",
    "    img_A = resize(img_A, [dim, dim])\n",
    "    img_B = resize(img_B, [dim, dim])\n",
    "    if not is_testing:\n",
    "        \n",
    "        if np.random.random() >= 0.5:\n",
    "            img_A = np.fliplr(img_A)\n",
    "            img_B = np.fliplr(img_B)\n",
    "\n",
    "    #print('2',img_A.max(),img_A.min())     \n",
    "    img_A = 2*(img_A/255.0) - 1\n",
    "    img_B = 2*(img_B/255.0) - 1\n",
    "\n",
    "    img_AB = np.concatenate((img_A, img_B), axis=2)\n",
    "\n",
    "    return img_AB\n",
    "\n",
    "\n",
    "def merge(images, size):\n",
    "    h, w = images.shape[1], images.shape[2]\n",
    "    img = np.zeros((h * size[0], w * size[1], 3))\n",
    "    for idx, image in enumerate(images):\n",
    "        i = idx % size[1]\n",
    "        j = idx // size[1]\n",
    "        img[j * h:j * h + h, i * w:i * w + w, :] = image\n",
    "\n",
    "    return img\n",
    "\n",
    "\n",
    "def image_save(images, size, path):\n",
    "    return imsave(path, merge(images, size))\n",
    "\n",
    "\n",
    "def save_images(images, size, image_path):\n",
    "    return image_save(inverse_transform(images), size, image_path)\n",
    "\n",
    "\n",
    "def inverse_transform(images):\n",
    "    return (images + 1) * 127.5\n",
    "\n",
    "\n",
    "class ImagePool(object):\n",
    "    def __init__(self, maxsize=50):\n",
    "        self.maxsize = maxsize\n",
    "        self.num_img = 0\n",
    "        self.images = []\n",
    "\n",
    "    def __call__(self, image):\n",
    "        if self.maxsize <= 0:\n",
    "            return image\n",
    "        if self.num_img < self.maxsize:\n",
    "            self.images.append(image)\n",
    "            self.num_img += 1\n",
    "            return image\n",
    "        if np.random.rand() > 0.5:\n",
    "            idx = int(np.random.rand() * self.maxsize)\n",
    "            tmp1 = copy.copy(self.images[idx])[0]\n",
    "            self.images[idx][0] = image[0]\n",
    "            idx = int(np.random.rand() * self.maxsize)\n",
    "            tmp2 = copy.copy(self.images[idx])[1]\n",
    "            self.images[idx][1] = image[1]\n",
    "            return [tmp1, tmp2]\n",
    "        else:\n",
    "            return image\n",
    "\n",
    "\n",
    "class customConv2D(layers.Layer):\n",
    "    def __init__(self, filters, kernel_size=4, strides=2, padding='SAME', norm=True, alpha=0.2, activation='lrelu'):\n",
    "        super(customConv2D, self).__init__()\n",
    "        self.norm = norm\n",
    "        self.conv1 = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=(strides, strides),\n",
    "                                   padding=padding)\n",
    "        if self.norm:\n",
    "            self.bnorm = layers.BatchNormalization()\n",
    "        if activation == 'lrelu':\n",
    "            self.activation = layers.LeakyReLU(alpha=alpha)\n",
    "        elif activation == 'relu':\n",
    "            self.activation = layers.ReLU()\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.conv1(x)\n",
    "        if self.norm:\n",
    "            x = self.bnorm(x)\n",
    "        x = self.activation(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class customDeConv2D(layers.Layer):\n",
    "    def __init__(self, filters, kernel_size=4, strides=2, padding='SAME', dropout_rate=0):\n",
    "        super(customDeConv2D, self).__init__()\n",
    "        self.dropout_rate = dropout_rate\n",
    "        self.deconv1 = layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=(strides, strides),\n",
    "                                              padding=padding)\n",
    "        if self.dropout_rate > 0:\n",
    "            self.drop = layers.Dropout(rate=dropout_rate)\n",
    "\n",
    "        self.bnorm = layers.BatchNormalization()\n",
    "        self.activation = layers.ReLU()\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.deconv1(x)\n",
    "        if self.dropout_rate > 0:\n",
    "            x = self.drop(x)\n",
    "        x = self.bnorm(x)\n",
    "        x = self.activation(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class generator(Model):\n",
    "    def __init__(self, gf, channels=3):\n",
    "        super(generator, self).__init__()\n",
    "        self.down1 = customConv2D(filters=gf, strides=2, norm=False)\n",
    "        self.down2 = customConv2D(filters=gf * 2, strides=2)\n",
    "        self.down3 = customConv2D(filters=gf * 4, strides=2)\n",
    "        self.down4 = customConv2D(filters=gf * 8, strides=2)\n",
    "        self.down5 = customConv2D(filters=100, strides=1, padding='VALID')\n",
    "\n",
    "        self.up1 = customDeConv2D(filters=gf * 8, strides=1, padding='VALID')\n",
    "        self.up2 = customDeConv2D(filters=gf * 4, strides=2)\n",
    "        self.up3 = customDeConv2D(filters=gf * 2, strides=2)\n",
    "        self.up4 = customDeConv2D(filters=gf, strides=2)\n",
    "        self.convf = layers.Conv2DTranspose(filters=channels, kernel_size=4, strides=(2, 2), padding='SAME',\n",
    "                                   activation=tf.nn.tanh)\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.down1(x)\n",
    "        #print(x.shape)\n",
    "        x = self.down2(x)\n",
    "        #print(x.shape)\n",
    "        x = self.down3(x)\n",
    "        #print(x.shape)\n",
    "        x = self.down4(x)\n",
    "        #print(x.shape)\n",
    "        x = self.down5(x)\n",
    "        #print('down 5',x.shape)\n",
    "\n",
    "        x = self.up1(x)\n",
    "        #print('up1',x.shape)\n",
    "        x = self.up2(x)\n",
    "        #print('up2',x.shape)\n",
    "        x = self.up3(x)\n",
    "        #print('up3',x.shape)\n",
    "        x = self.up4(x)\n",
    "        #print('up4',x.shape)\n",
    "        x = self.convf(x)\n",
    "        #print('upf',x.shape)\n",
    "        return x\n",
    "\n",
    "\n",
    "class discriminator(Model):\n",
    "\n",
    "    def __init__(self, df):\n",
    "        super(discriminator, self).__init__()\n",
    "        self.down1 = customConv2D(filters=df, strides=2, norm=False)\n",
    "        self.down2 = customConv2D(filters=df * 2, strides=2)\n",
    "        self.down3 = customConv2D(filters=df * 4, strides=2)\n",
    "        self.down4 = customConv2D(filters=df * 8, strides=2)\n",
    "        self.down5 = layers.Conv2D(filters=1, kernel_size=4, strides=1, padding='VALID')\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.down1(x)\n",
    "        x = self.down2(x)\n",
    "        x = self.down3(x)\n",
    "        x = self.down4(x)\n",
    "        x = self.down5(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "\n",
    "class GAN_X2Y(Model):\n",
    "    def __init__(self, G_XY, D_Y):\n",
    "        super(GAN_X2Y, self).__init__()\n",
    "        self.G_XY = G_XY\n",
    "        self.D_Y = D_Y\n",
    "\n",
    "    def call(self, x):\n",
    "        fake_x = self.G_XY(x)\n",
    "        x = self.D_Y(x)\n",
    "        return fake_x,x\n",
    "\n",
    "\n",
    "\n",
    "def process_data(data_dir,skip_preprocess=False):\n",
    "    \"\"\"\n",
    "    Split the images into domain A and domain B images\n",
    "    Each image contain both Domain A and Domain B images together\n",
    "    This routines splits it up\n",
    "    :param data_dir: Input images dir \n",
    "    :return: \n",
    "    \"\"\"\n",
    "    assert Path(data_dir).exists()\n",
    "    \n",
    "    domain_A_dir = f'{Path(data_dir)}/trainA'\n",
    "    domain_B_dir = f'{Path(data_dir)}/trainB'\n",
    "    if skip_preprocess:\n",
    "        return domain_A_dir, domain_B_dir\n",
    "    os.makedirs(domain_A_dir,exist_ok=True)\n",
    "    os.makedirs(domain_B_dir,exist_ok=True)\n",
    "    files = os.listdir(Path(data_dir))\n",
    "    print(f'Images to process: {len(files)}')\n",
    "    \n",
    "    i = 0\n",
    "    for fl in files:\n",
    "        i += 1\n",
    "        try:\n",
    "            img = imread(f\"{Path(data_dir)}/{str(fl)}\")\n",
    "            #print(img.shape)\n",
    "            w, h, d = img.shape\n",
    "            img_A = img[:w, :int(h / 2), :d]\n",
    "            img_B = img[:w, int(h / 2):h, :d]\n",
    "            imsave(f\"{data_dir}/trainA/{fl}_A.jpg\", img_A)\n",
    "            imsave(f\"{data_dir}/trainB/{fl}_B.jpg\", img_B)\n",
    "            if (i % 10000) == 0 & (i >= 10000):\n",
    "                print(f\"processed {i+1} images\")\n",
    "        except:\n",
    "            print(f\"Skip processing image {Path(data_dir)}/{str(fl)}\")\n",
    "\n",
    "    return domain_A_dir, domain_B_dir\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def train(data_dir,sample_dir,num_epochs=5,lr=0.0002,beta1=0.5,beta2=0.99,train_size=10000,batch_size=64,epoch_intermediate=10,dim=64,sample_freq=5,_lambda_=0.5,skip_preprocess=False):\n",
    "    \n",
    "    # Process input data and split to domain A, domain B data\n",
    "    domain_A_dir, domain_B_dir = process_data(data_dir=data_dir,skip_preprocess=skip_preprocess)\n",
    "    \n",
    "    # Build the models\n",
    "    G_AB, G_BA = generator(gf=64), generator(gf=64)\n",
    "    D_A, D_B = discriminator(df=64), discriminator(df=64)\n",
    "    GAN_AB = GAN_X2Y(G_XY=G_AB,D_Y=D_B)\n",
    "    GAN_BA = GAN_X2Y(G_XY=G_BA,D_Y=D_A)\n",
    "    G_AB_g, G_BA_g, D_A_g, D_B_g, GAN_AB_g,  GAN_BA_g =  tf.function(G_AB), tf.function(G_BA), \\\n",
    "                tf.function(D_A), tf.function(D_B), tf.function(GAN_AB),  tf.function(GAN_BA)\n",
    "    \n",
    "    # Setup the imagepool\n",
    "    pool = ImagePool()\n",
    "    \n",
    "    loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.NONE)\n",
    "    for epoch in range(num_epochs):\n",
    "        data_A = os.listdir(domain_A_dir)\n",
    "        data_B = os.listdir(domain_B_dir)\n",
    "        data_A = [f\"{domain_A_dir}/{str(x)}\" for x in data_A]\n",
    "        data_B = [f\"{domain_B_dir}/{str(x)}\" for x in data_B]\n",
    "        np.random.shuffle(data_A)\n",
    "        np.random.shuffle(data_B)\n",
    "        \n",
    "        if not train_size:\n",
    "            train_size = min(len(data_A), len(data_B))\n",
    "        num_batches = min(len(data_A), len(data_B),train_size)\n",
    "        \n",
    "        # Setup lr based on the schedule\n",
    "        lr_curr =  lr if epoch < epoch_intermediate else lr* (num_epochs - epoch) / (num_epochs - epoch_intermediate)\n",
    "        # Set the optimizer\n",
    "        optimizer = tf.keras.optimizers.Adam(lr_curr,beta_1=beta1,beta_2=beta2)\n",
    "                \n",
    "        for i in range(num_batches):\n",
    "            batch_files = list(zip(data_A[i*batch_size:(i + 1)* batch_size],\n",
    "                                       data_B[i*batch_size:(i + 1)* batch_size]))\n",
    "            batch_images = [load_train_data(batch_file, dim) for batch_file in batch_files]\n",
    "            batch_images = np.array(batch_images).astype(np.float32)\n",
    "            image_real_A = tf.constant(batch_images[:,:,:,:3])\n",
    "            image_real_B = tf.constant(batch_images[:,:,:,3:6])\n",
    "            #print(image_real_A.shape,image_real_B.shape)\n",
    "            \n",
    "            \n",
    "            with tf.GradientTape(persistent=True) as tape:\n",
    "                \n",
    "                fake_AB, logit_fake_AB = GAN_AB_g(image_real_A)\n",
    "                fake_BA, logit_fake_BA = GAN_BA_g(image_real_B)\n",
    "                #print(fake_AB.shape,fake_BA.shape)\n",
    "                A_reconst = G_BA_g(fake_AB)\n",
    "                B_reconst = G_AB_g(fake_BA)\n",
    "                logit_real_D_B = D_B_g(image_real_B)\n",
    "                logit_real_D_A = D_A_g(image_real_A)\n",
    "                print(logit_fake_AB.shape)\n",
    "                D_B_loss_fake = tf.reduce_mean(loss_fn(tf.zeros_like(logit_fake_AB),logit_fake_AB))   \n",
    "                D_B_loss_real = tf.reduce_mean(loss_fn(tf.ones_like(logit_real_D_B),logit_real_D_B))\n",
    "                D_B_loss   = 0.5*(D_B_loss_fake + D_B_loss_real)\n",
    "                \n",
    "                D_A_loss_fake   = tf.reduce_mean(loss_fn(tf.zeros_like(logit_fake_BA),logit_fake_BA))   \n",
    "                D_A_loss_real   = tf.reduce_mean(loss_fn(tf.ones_like(logit_real_D_A),logit_real_D_A))\n",
    "                D_A_loss   = 0.5*(D_A_loss_fake + D_A_loss_real)\n",
    "                loss_discriminator = D_B_loss + D_A_loss\n",
    "                \n",
    "                loss_G_ABA = _lambda_*tf.reduce_mean(tf.abs(A_reconst - image_real_A))\n",
    "                loss_G_A_DB  = tf.reduce_mean(loss_fn(tf.ones_like(logit_fake_AB),logit_fake_AB))\n",
    "                loss_G_AB     =  loss_G_ABA + loss_G_A_DB\n",
    "                \n",
    "                loss_G_BAB = _lambda_*tf.reduce_mean(tf.abs(B_reconst - image_real_B))\n",
    "                loss_G_B_DA  = tf.reduce_mean(loss_fn(tf.ones_like(logit_fake_BA),logit_fake_BA))\n",
    "                loss_G_BA     =  loss_G_BAB + loss_G_B_DA\n",
    "                \n",
    "                loss_generator = loss_G_AB + loss_G_BA\n",
    "            # Compute gradient\n",
    "            print(f\"Epoch, iter {epoch,i}:  D_B_loss:{D_B_loss_fake.numpy(),D_B_loss_real.numpy()},D_A_loss:{D_A_loss_fake.numpy(),D_A_loss_real.numpy()}, \\\n",
    "                  loss_G_AB:{loss_G_ABA.numpy(),loss_G_A_DB.numpy()},loss_G_BA:{loss_G_BAB.numpy(),loss_G_B_DA.numpy()}\")\n",
    "            grad_D_A = tape.gradient(D_A_loss, D_A.trainable_variables)\n",
    "            grad_D_B = tape.gradient(D_B_loss, D_B.trainable_variables)\n",
    "            grad_G_AB = tape.gradient(loss_G_AB,G_AB.trainable_variables)\n",
    "            grad_G_BA = tape.gradient(loss_G_BA,G_BA.trainable_variables)\n",
    "            # update the parameters\n",
    "            optimizer.apply_gradients(zip(grad_D_A, D_A.trainable_variables))\n",
    "            optimizer.apply_gradients(zip(grad_D_B, D_B.trainable_variables))\n",
    "            optimizer.apply_gradients(zip(grad_G_AB, G_AB.trainable_variables))\n",
    "            optimizer.apply_gradients(zip(grad_G_BA, G_BA.trainable_variables))\n",
    "            del tape\n",
    "            if i % sample_freq == 1:\n",
    "                sample_model(sample_dir,data_dir, epoch, i,GAN_AB_g,GAN_BA_g)\n",
    "    return G_AB, G_BA, D_A, D_B, GAN_AB,  GAN_BA \n",
    "            \n",
    "\n",
    "            \n",
    "def sample_model(sample_dir,data_dir,epoch, batch_num,GAN_AB_g,GAN_BA_g,batch_size=64,dim=64):\n",
    "    assert sample_dir != None\n",
    "    if not Path(sample_dir).exists():\n",
    "        os.makedirs(f\"{Path(sample_dir)}\")\n",
    "    data_A = os.listdir(f\"{data_dir}/trainA\")\n",
    "    data_B = os.listdir(f\"{data_dir}/trainB\") \n",
    "    data_A = [f\"{Path(data_dir)}/trainA/{str(file_name)}\" for file_name in data_A ]\n",
    "    data_B = [f\"{Path(data_dir)}/trainB/{str(file_name)}\" for file_name in data_B ]\n",
    " \n",
    "\n",
    "    np.random.shuffle(data_A)\n",
    "    np.random.shuffle(data_B)\n",
    "    batch_files = list(zip(data_A[:batch_size], data_B[:batch_size]))\n",
    "    sample_images = [load_train_data(batch_file, is_testing=True,dim=dim) for batch_file in batch_files]\n",
    "    sample_images = np.array(sample_images).astype(np.float32)\n",
    "    image_real_A = tf.constant(sample_images[:,:,:,:3])\n",
    "    image_real_B = tf.constant(sample_images[:,:,:,3:6])\n",
    "        \n",
    "    fake_AB, logit_fake_AB = GAN_AB_g(image_real_A,training=False)\n",
    "    fake_BA, logit_fake_BA = GAN_BA_g(image_real_B,training=False)\n",
    "\n",
    "        \n",
    "    save_images(fake_AB, [batch_size, 1],\n",
    "                    '{}/A_{:02d}_{:04d}.jpg'.format(sample_dir, epoch, batch_num))\n",
    "    save_images(fake_BA, [batch_size, 1],\n",
    "                    '{}/B_{:02d}_{:04d}.jpg'.format(sample_dir, epoch, batch_num))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1edde63d-a4ba-473b-9fcd-e8f1f474450d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 0):  D_B_loss:(0.69508344, 0.686507),D_A_loss:(0.6730642, 0.7194115),                   loss_G_AB:(0.46526447, 0.6912416),loss_G_BA:(0.3900148, 0.7137891)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 1):  D_B_loss:(0.49095875, 0.7051947),D_A_loss:(0.7827076, 0.4048879),                   loss_G_AB:(0.45925343, 0.9471139),loss_G_BA:(0.3837862, 0.618662)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [127.42203521728516, 127.57080078125]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [127.40267181396484, 127.5905990600586]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 2):  D_B_loss:(0.27081397, 0.6719996),D_A_loss:(0.7032045, 0.24742278),                   loss_G_AB:(0.4633524, 1.4409294),loss_G_BA:(0.37738752, 0.72340584)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 3):  D_B_loss:(0.18053752, 0.3243001),D_A_loss:(0.26687366, 0.3152774),                   loss_G_AB:(0.4643192, 1.8075671),loss_G_BA:(0.37916365, 1.6961589)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 4):  D_B_loss:(0.03379266, 0.38061675),D_A_loss:(0.9265365, 0.00545401),                   loss_G_AB:(0.46698955, 3.4561038),loss_G_BA:(0.3872371, 1.3907706)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 5):  D_B_loss:(3.5459814, 0.004144286),D_A_loss:(0.067838594, 0.37928712),                   loss_G_AB:(0.4652398, 0.030384548),loss_G_BA:(0.3740566, 4.648263)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 6):  D_B_loss:(0.008729804, 0.6237426),D_A_loss:(0.4356902, 0.026089786),                   loss_G_AB:(0.46177065, 4.845218),loss_G_BA:(0.37831223, 3.3665636)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [127.42994689941406, 127.628173828125]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [127.35110473632812, 127.63658142089844]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 7):  D_B_loss:(0.008502044, 0.45221853),D_A_loss:(0.20851347, 0.04516188),                   loss_G_AB:(0.46265912, 4.85422),loss_G_BA:(0.37699184, 5.6886578)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 8):  D_B_loss:(0.027725698, 0.24376392),D_A_loss:(0.08753133, 0.101818755),                   loss_G_AB:(0.46405065, 3.639647),loss_G_BA:(0.363703, 6.523994)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 9):  D_B_loss:(0.059499335, 0.24102141),D_A_loss:(0.17179969, 0.053086847),                   loss_G_AB:(0.46795055, 2.8830786),loss_G_BA:(0.38716006, 7.9105206)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 10):  D_B_loss:(0.09899663, 0.19808811),D_A_loss:(0.112789065, 0.046570763),                   loss_G_AB:(0.46276233, 2.3921564),loss_G_BA:(0.39134112, 8.874407)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 11):  D_B_loss:(0.10427531, 0.13639225),D_A_loss:(0.07371171, 0.05669458),                   loss_G_AB:(0.46176758, 2.3481197),loss_G_BA:(0.37256035, 10.692169)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [127.4099349975586, 127.81310272216797]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [127.22187805175781, 127.70793914794922]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 12):  D_B_loss:(0.05396077, 0.17099619),D_A_loss:(0.07773225, 0.018943598),                   loss_G_AB:(0.46641636, 2.9853036),loss_G_BA:(0.38258198, 9.796209)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 13):  D_B_loss:(0.044760466, 0.12814097),D_A_loss:(0.012586977, 0.045858465),                   loss_G_AB:(0.4658762, 3.209349),loss_G_BA:(0.38856626, 13.122021)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 14):  D_B_loss:(0.03660918, 0.15828699),D_A_loss:(0.15538499, 0.0036359916),                   loss_G_AB:(0.46655008, 3.3952396),loss_G_BA:(0.3795905, 14.149378)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 15):  D_B_loss:(0.04858035, 0.09949057),D_A_loss:(0.022862548, 0.07816471),                   loss_G_AB:(0.46481887, 3.1708302),loss_G_BA:(0.38139138, 15.3572445)\n",
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 16):  D_B_loss:(0.044765662, 0.1749599),D_A_loss:(0.22376476, 0.0028392074),                   loss_G_AB:(0.4627923, 3.2056642),loss_G_BA:(0.3812717, 14.615754)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [127.2298583984375, 128.3085479736328]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [127.07259368896484, 127.7942886352539]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 1, 1, 1)\n",
      "Epoch, iter (0, 17):  D_B_loss:(0.06934464, 0.08779256),D_A_loss:(0.03619539, 0.043826856),                   loss_G_AB:(0.46549, 2.8511744),loss_G_BA:(0.36506996, 16.40044)\n"
     ]
    }
   ],
   "source": [
    "data_dir='/media/santanu/9eb9b6dc-b380-486e-b4fd-c424a325b976/edges2handbags/train'\n",
    "sample_dir='/home/santanu/generated_samples'\n",
    "G_AB_g, G_BA_g, D_A_g, D_B_g, GAN_AB_g,  GAN_BA_g  = train(data_dir=data_dir,sample_dir=sample_dir,skip_preprocess=True)         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ee8104d-0835-430c-b32c-14a1fb28f65c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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